Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
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Abstract
We find that current computational methods for estimating transcript abundance from RNA-seq data can lead to hundreds of false-positive results. We show that these systematic errors stem largely from a failure to model fragment GC content bias. Sample-specific biases associated with fragment sequence features lead to misidentification of transcript isoforms. We introduce alpine, a method for estimating sample-specific bias-corrected transcript abundance. By incorporating fragment sequence features, alpine greatly increases the accuracy of transcript abundance estimates, enabling a fourfold reduction in the number of false positives for reported changes in expression compared with Cufflinks. Using simulated data, we also show that alpine retains the ability to discover true positives, similar to other approaches. The method is available as an R/Bioconductor package that includes data visualization tools useful for bias discovery.
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- Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Lightweight quantification methods produced the same type of isoform-switching errors as Cufflinks, RSEM, and MISO.
“The lightweight quantification methods generated the same kind of isoform-switching errors as Cufflinks and RSEM (Supplementary Figures 14-15), and as MISO 27, a statistical method for estimating isoform percentages within multi-isoform genes”
Quote in the paperSubstantial claim - Findingreasoning§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
In USF2, lower coverage in center 1 samples shifts expression estimates from NM003367 to NM207291, which lacks the high-GC exon.
“the drop in coverage for samples from center 1 results in a shift in expression estimates from NM003367 to NM207291, which does not include the high GC exon”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Overall mixing ratio recovery for all methods was higher for poly-A selected samples (65-75%) than for ribosomal RNA depleted samples (45-65%).
“Overall, the mixing ratio recovery for all methods was higher for poly-A selected samples (65-75%) compared to ribosomal RNA depleted samples (45-65%).”
Quote in the paperNot a substantial claim- Rests on an assumption: The expected mixing ratios in the ABRF dataset are accurate ground truth for assessing measurement accuracy.
- Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Correcting for fragment sequence bias with alpine reduced extreme predicted changes in isoform percent across centers among 2,838 two-isoform genes.
“Correcting for fragment sequence bias using alpine reduced the number of extreme predicted changes in isoform percent when comparing across center”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
RSEM showed similar rates of across-center differentially expressed transcripts.
“RSEM had similar rates of differentially expressed transcripts across center”
Quote in the paperSubstantial claim
- Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Using only read-start bias terms in alpine produced no visible improvement.
“Using only read start bias terms in the alpine model did not provide visible improvements”
Quote in the paperNot a substantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Center 1 samples have much lower representation of high-GC fragments than center 2, even after adjusting for random hexamer priming bias.
“The samples from center 1 have dramatically reduced representation of high GC fragments compared to center 2, even after adjusting for differences due to random hexamer priming bias”
Quote in the paperSubstantial claim - Findingnone given§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Strong GC content bias was found in some samples across all public datasets examined.
“Strong GC content bias was found for some samples for all public datasets examined.”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Fragment GC content dependence was strongly associated with library preparation location, not sequencing location.
“We found that fragment GC content dependence was strongly associated with the location of library preparation, and not with the location of sequencing”
Quote in the paperSubstantial claim- Rests on an assumption: The SEQC samples prepared at different sites are comparable except for library preparation location.
- Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
When requiring BH-adjusted p<1% and log2 fold change above 0.5, 1, or 2, alpine had a lower false-positive percentage among 5,676 transcripts than all other methods.
“When restricting positives to those transcripts with Benjamini-Hochberg adjusted p value less than 1% and additionally requiring that the log2 fold change be above a threshold (0.5, 1, or 2), alpine consistently had a lower percent of false positives out of the set of 5,676 transcripts than all other methods”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Fragment sequence models accurately capture coverage drops that read-start sequence models miss.
“The fragment sequence models accurately capture the drops in coverage that were not captured by the read start sequence models”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Random permutations of samples yielded no transcripts with differential expression.
“as random permutations of samples resulted in no transcripts with differential expression.”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Among 5,716 transcripts from two-isoform genes with at least one isoform FPKM above 0.1, Cufflinks reported 566 as differentially expressed across centers at 1% FDR.
“Out of 5,716 transcripts from genes with two isoforms in which at least one isoform had FPKM greater than 0.1, 566 transcripts reported differential expression across center according to Cufflinks estimated abundances, at a target FDR of 1%.”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
For genes with one or more isoforms reported as differentially expressed, isoform-exclusive regions had higher GC content (mode 70% vs 50%) than chance, Wilcoxon p<0.0001.
“For genes with one or more isoforms reported differentially expressed, the regions of the isoform that were exclusive to one or the other isoform had much higher GC-content (mode at 70% compared to 50%) than expected by chance (Figure 2b, Wilcoxon p < 0.0001).”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Current computational methods for estimating transcript abundance from RNA-seq data can yield hundreds of false-positive findings.
“We find that current computational methods for estimating transcript abundance from RNA-seq data can lead to hundreds of false-positive results.”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
In 5,676 two-isoform transcripts, adding fragment GC and GC stretch bias terms reduced false positives at 1% FDR fourfold: Cufflinks 562 vs alpine 141.
“We found that including bias terms for fragment GC and GC stretches resulted in a fourfold decrease in the number of false positives at a target FDR of 1%: Cufflinks reported 562 differentially expressed transcripts, while alpine reported 141”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
In across-protocol comparisons, alpine reported the fewest false positives at 1% FDR and 1% FWER.
“In a comparison of expression estimates across protocol, alpine reported the fewest number of false positives, controlling at 1% FDR and at 1% FWER”
Quote in the paperNot a substantial claim- Rests on an assumption: The ABRF samples prepared with different protocols are comparable except for the protocol.
- Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
In simulated data, alpine keeps the ability to find true positives at a level similar to other methods.
“Using simulated data, we also show that alpine retains the ability to discover true positives, similar to other approaches.”
Quote in the paperNot a substantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
In 30 GEUVADIS lymphoblastoid samples, Cufflinks with sequence bias removal reported 2,510 of 25,588 transcripts (10%) with average FPKM above 0.1 as differentially expressed across centers at a 1% FDR target.
“Performing a t-test on log2(FPKM + 1) values from Cufflinks across centers, and filtering on Benjamini-Hochberg adjusted p values for a target 1% false discovery rate (FDR) resulted in 2,510 transcripts out of 25,588 (10%) with average FPKM greater than 0.1 reported as differentially expressed”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
The sequence features included in the model provided substantial improvements over existing methods.
“While the sequence features we included in our model provided substantial improvements over existing methods”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Among 6,761 multi-isoform genes with average FPKM above 0.1 for at least one isoform, 619 changed their reported major isoform across centers when using Cufflinks FPKM.
“When comparing across center, 619 out of 6,761 genes with multiple isoforms and average FPKM greater than 0.1 for one or more isoform had changes in the reported major isoform across centers using FPKM values estimated by Cufflinks”
Quote in the paperSubstantial claim - Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Cufflinks, kallisto, Salmon, and Sailfish, all run with read-start sequence bias correction, gave similar results.
“Note that four of the methods evaluated, Cufflinks, kallisto, Salmon, and Sailfish all were run with read start sequence bias correction turned on (Supplementary Note) and obtained similar results”
Quote in the paperNot a substantial claim - Interpretationreasoning§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Sample-specific biases tied to fragment sequence features cause transcript isoforms to be misidentified.
“Sample-specific biases associated with fragment sequence features lead to mis-identification of transcript isoforms.”
Quote in the paperSubstantial claim - Premisecitation§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
At the transcript level, current methods correct for biases such as fragment length distribution from size selection, positional bias from RNA degradation and mRNA selection, and read-start sequence bias from random hexamer primer binding.
“At the transcript level, sample-specific biases that current methods correct for include the fragment length distribution induced by size selection, positional bias along the transcript due to RNA degradation and mRNA selection techniques, and sequence-based bias in read start positions arising from the differential binding efficiency of random hexamer primers2,12-16”
Quote in the paperSubstantial claim0 of 6 sources checked- cites [2] Li, B., Ruotti, V., Stewart, R.M., Thomson, J.A. & Dewey, C.N. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26, 493–500 (2010). source
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- cites [12] Li, J., Jiang, H. & Wong, W.H. Modeling non-uniformity in short-read rates in RNA-Seq data. Genome Biol. 11, R50 (2010). source
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- cites [13] Hansen, K.D., Brenner, S.E. & Dudoit, S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 38, e131 (2010). source
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- cites [14] Roberts, A., Trapnell, C., Donaghey, J., Rinn, J.L. & Pachter, L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 12, R22–R14 (2011). source
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- cites [15] Nicolae, M., Mangul, S., Maˇndoiu, I.I. & Zelikovsky, A. Estimation of alternative splicing isoform frequencies from RNA-Seq data. Algorithms Mol. Biol. 6, 9 (2011). source
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- cites [16] Li, W. & Jiang, T. Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads. Bioinformatics 28, 2914–2921 (2012). source
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- Premisecitation§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
RNA-seq data can contain sample-specific biases arising from RNA extraction and library preparation.
“However, RNA-seq data can suffer from sample-specific biases as a result of RNA extraction and library preparation steps3–5.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [3] 't Hoen, P.A. et al. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat. Biotechnol. 31, 1015–1022 (2013). source
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- cites [4] Su, Z. & SEQC/MAQC-III Consortium A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903–914 (2014). source
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- cites [5] Li, S. et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat. Biotechnol. 32, 888–895 (2014). source
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- Interpretationreasoning§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
The findings reflect general systematic errors, not only batch effects.
“We note that our findings reflect general systematic errors and not just differences induced by batch effects.”
Quote in the paperSubstantial claim - Interpretationnone given§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Technical-bias-induced fold changes are often larger than fold changes of biological interest.
“because fold changes induced by technical bias are often larger than those potentially of biological interest.”
Quote in the paperSubstantial claim - Interpretationown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Nearly all of the 2,510 transcripts reported as differentially expressed across centers are expected to be false positives.
“However, we expect that nearly all of the 2,510 transcripts reporting differential expression are false positives”
Quote in the paperSubstantial claim- Rests on an assumption: The 30 GEUVADIS samples from the two sequencing centers have no true biological differences across centers, so across-center differential expression is technical.
- Premisecitation§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Long high-GC stretches within a fragment can affect whether that fragment is amplified and sequenced.
“Additionally, stretches of high GC content sequences within a fragment can have an influence on whether a fragment will be amplified and sequenced 23.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Hron, T., Pajer, P., Pačes, J., Baru˚tneˇk, P. & Elleder, D. Hidden genes in birds. Genome Biol. 16, 164 (2015). source
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- Interpretationown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
The systematic errors in transcript abundance estimates are mostly due to not modeling fragment GC content bias.
“We show that these systematic errors stem largely from a failure to model fragment GC content bias.”
Quote in the paperSubstantial claim - Interpretationown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Experiments with strong dependence of fragment rate on fragment GC content are likely to have many incorrectly reported major isoforms and biased abundance estimates unless explicitly corrected by methods that model fragment sequence bias.
“There are likely to be many incorrectly reported major isoforms and biased abundance estimates for experiments that show strong dependence of the fragment rate on fragment GC content (e.g. Figure 2e), unless these are explicitly corrected for using methods that model fragment sequence bias.”
Quote in the paperSubstantial claim - Interpretationreasoning
Because the C and D samples were made by mixing the A and B samples in 3:1 and 1:3 ratios, comparing C/D with A/B across methods provides a measure of accuracy in transcript abundance estimation.
“a comparison of C/D and A/B for the different methods can be used to generate a measure of accuracy in estimating transcript abundance.”
Quote in the paperSubstantial claim- Rests on an assumption: The C and D samples in the ABRF dataset were made by mixing the A and B samples in 3:1 and 1:3 ratios, respectively.
- Premisecitation§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
't Hoen et al. observed that slight differences in average GC content across samples lead to differences in transcript quantification.
“Indeed, ’t Hoen et al.3 observed that slight differences in average GC content across samples lead to differences in quantification for transcripts.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites ’t Hoen et al.3
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- May combine two claims. 21
- Classified as probably not a substantial claim. 5
May combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationIn simulations where condition was confounded with sequencing center, alpine had the highest sensitivity for a given specificity; in the balanced design, methods performed similarly.
“alpine had the highest sensitivity for a given specificity in the confounded design (Figure 3g-h), while methods performed similarly for the balanced design”
Quote in the paperSubstantial claim- Rests on an assumption: The Polyester-simulated RNA-seq data, with fragment GC dependence estimated from GEUVADIS samples, adequately represents real RNA-seq bias for sensitivity analysis.
May combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationMISO was more consistent than existing transcript abundance estimators at isoform identification, but alpine reported less than half of the large across-center isoform switches MISO reported.
“While MISO performed better than the existing transcript abundance estimators in consistency of isoform identification, alpine reported less than half of the large isoform switches across center reported by MISO”
Quote in the paperSubstantial claimMay combine two claims.
Findingcitation§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationThe GC content effect is often nonlinear and highly sample-specific, so sample-specific bias estimation with smooth functions of fragment GC content is needed.
“we confirmed other findings 21, that the GC content effect was often nonlinear and highly sample-specific, therefore requiring sample-specific bias estimation using smooth functions of fragment GC content.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [21] Benjamini, Y. & Speed, T.P. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationFitting the fragment sequence model takes more computational effort than other models, though the implementation ran in comparable time to Cufflinks cuffquant.
“Fitting the fragment sequence model does require more computational effort than other models, though our implementation ran in comparable time to the cuffquant step of the Cufflinks suite”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationAdding GC stretch information to the fragment GC model improved predictive power, but only slightly.
“The model that also included the information about GC stretches was more predictive than the model with just the fragment GC content, although only slightly so.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationAlpine had the highest accuracy in estimating true expression values compared with other methods, with low median absolute error in isoform percent for two-isoform genes, comparable to RSEM.
“alpine had the highest accuracy in estimating the true expression values compared to the other methods (Supplementary Figures 18-19), with low median absolute error in estimating the percent of isoform abundance for genes with two isoforms, comparable to RSEM”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationWhen counting transcripts whose C/D ratio was within 10% of expected, RSEM had the highest recovery for both protocols, while alpine was consistently high (within 5% of the top method).
“When quantifying the number of transcripts whose C/D ratio was within 10% of the expected value, RSEM had the highest recovery for both protocols, though alpine also had consistently high recovery (within 5% of the top method).”
Quote in the paperNot a substantial claim- Rests on an assumption: The expected mixing ratios in the ABRF dataset are accurate ground truth for assessing measurement accuracy.
May combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationModels with fragment GC content doubled the predictive power of read-start bias models, including Cufflinks VLMM within alpine and mseq, which performed similarly.
“The models that included fragment GC content doubled the predictive power of the read start bias models, both the Cufflinks VLMM read start bias model implemented within alpine and mseq, which performed similarly”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationAlpine greatly reduced across-center significant differences while within-center coefficient of variation stayed the same as Cufflinks.
“In general, alpine greatly reduced across-center significant differences while within-center coefficient of variation of abundance estimates remained the same as for Cufflinks”
Quote in the paperSubstantial claimMay combine two claims.
Findingcitation§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationAcross ABRF protocols, the shape of fragment GC dependence changed little, but positional bias showed a strong effect, with poly-A selected samples having highest coverage at the 3' end.
“We observed little change in the shape of fragment GC dependence across protocol, and a strong effect in the positional bias, with poly-A selected samples having highest coverage at the 3’ end of transcripts, as reported by the ABRF study authors29”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29] Li, S. et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study. Nat. Biotechnol. 32, 915–925 (2014). source
Not checked in the free run.
- Rests on an assumption: The ABRF samples prepared with different protocols are comparable except for the protocol.
May combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationAlpine, by using fragment sequence features, substantially improves transcript abundance accuracy and cuts false positives for expression changes fourfold relative to Cufflinks.
“By incorporating fragment sequence features, alpine greatly increases the accuracy of transcript abundance estimates, enabling a fourfold reduction in the number of false positives for reported changes in expression compared with Cufflinks.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationWith Bonferroni correction at 1% FWER, Cufflinks reported 157 across-center DE transcripts and alpine reported 37.
“Using a more conservative Bonferroni correction, Cufflinks reported 157 differentially expressed transcripts across center with FWER of 1%, while alpine reported only 37.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningRNA-seq experiments commonly involve total or partial confounding, unknown batches, and sample-specific within-batch deviations, none of which the balanced design simulation captures.
“In contrast, scenarios with total or partial confounding, unknown batches, and sample-specific deviations within batches are typical in RNA-seq experiments and not represented by the balanced design simulation.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningTo avoid unequal final library sizes from discarded read pairs, the simulation was repeated with a higher initial target library size so that the expected final size was 60 million paired-end reads.
“As the process of discarding pairs of reads would result in unequal final library size, the simulation process was repeated, after first scaling the initial target library size higher such that the final library size for the experiment would be equal to 60 million paired-end reads in expectation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenIn the specificity analysis, alpine was run with bias correction for fragment length, relative position, and fragment GC content, and the other methods were run with their bias correction options enabled, including a positional bias correction for RSEM.
“The alpine software was run with bias correction terms for fragment length, relative position, and fragment GC content, and other methods were run with their bias correction arguments turned on, including a positional bias correction term for RSEM (Supplementary Note).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationFor DNA-seq, GC bias is best corrected by modeling at the fragment scale.
“For DNA-seq, this bias is best corrected by modeling at the scale of the fragment21.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [21] Benjamini, Y. & Speed, T.P. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012). source
Not checked in the free run.
May combine two claims.
Study design and conductown dataThe GC content curves used in the simulation were estimated after read start bias had been removed with the Cufflinks VLMM, so the simulated fragment sequence effect does not stand in for read start bias.
“Note that the GC content curves used in the simulation (Figure 2e) were estimated after removing the read start bias using the Cufflinks VLMM, and so the fragment sequence effect observed is not a proxy for read start bias.”
Quote in the paperSubstantial claimMay combine two claims.
Premisecitation§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationExtreme technical, sample-specific variability in coverage along transcripts is common, is not explained by current bias models, and confounds transcript identification and quantification.
“Even so, it is common to observe extreme variability in the coverage of RNA-seq fragments along transcripts that is purely technical and sample-specific17 and not explained by current bias models, which confounds current methods designed to identify and quantify transcripts18”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [17] Lahens, N.F. et al. IVT-seq reveals extreme bias in RNA sequencing. Genome Biol. 15, R86 (2014). source
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- cites [18] Hayer, K.E., Pizarro, A., Lahens, N.F., Hogenesch, J.B. & Grant, G.R. Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data. Bioinformatics 31, 3938–3945 (2015). source
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May combine two claims.
Interpretationnone given§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationThe identified problem applies to within-sample transcript abundance estimates for samples from a single center and for small-scale experiments.
“The problem we identify holds for within-sample transcript abundance estimates for samples from a single center and for samples in small-scale experiments.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationThe study demonstrated specificity using data from different sequencing centers and sensitivity using simulation.
“Here we demonstrated specificity using data prepared by different sequencing centers, and sensitivity using simulation.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe balanced design with sequencing center as a blocking factor is the best-case scenario for the competing methods, because the batches were known exactly and the residual degrees of freedom was high, allowing batch effects on transcript abundance estimates to be precisely estimated and removed by the linear model.
“Note that the balanced design with blocking factor represents the best-case scenario for the competing methods, as the batches were known exactly and the residual degrees of freedom was high, such that the batch effects on transcript abundance estimates could be precisely estimated and removed by the linear model.”
Quote in the paperNot a substantial claimMay combine two claims.
Premisecitation§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationGC content bias is often seen in high-throughput sequencing, with some GC fragments under-represented, and can be partly attributed to PCR amplification during library preparation.
“A GC content bias is often observed in high-throughput sequencing data, with fragments of certain GC content under-represented, and can be partially attributed to PCR amplification during library preparation20.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [20] Aird, D. et al. Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol. 12, R18 (2011). source
Not checked in the free run.
May combine two claims.
Interpretationown data§ Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimationAcross protocols, all methods including alpine reported 10,000-14,000 false-positive abundance differences at 1% FDR out of 28,000, suggesting none can remove the bulk of systematic bias across protocol.
“The large number of transcripts with false positive differences in abundance reported across protocol, in the range of 10-14,000 at 1% FDR for all methods out of 28,000, suggests that none of the existing methods evaluated including alpine can remove the bulk of systematic bias seen across protocol.”
Quote in the paperSubstantial claim- Rests on an assumption: The ABRF samples prepared with different protocols are comparable except for the protocol.
The paper relies on this without support.
The 30 GEUVADIS samples from the two sequencing centers have no true biological differences across centers, so across-center differential expression is technical.
“We downloaded 30 RNA-seq samples from the GEUVADIS Project of lymphoblastoid cell lines derived from the Toscani population, 15 of which were sequenced at one center and 15 at another19”
The paper relies on this without support.
The Polyester-simulated RNA-seq data, with fragment GC dependence estimated from GEUVADIS samples, adequately represents real RNA-seq bias for sensitivity analysis.
“we performed a sensitivity analysis using an RNA-seq fragment simulator, Polyester 28, to generate 30 samples with fragment GC content dependence estimated from the 30 GEUVADIS samples, after having removed read start sequence bias”
The paper relies on this without support.
The expected mixing ratios in the ABRF dataset are accurate ground truth for assessing measurement accuracy.
“The relationship of the samples in the ABRF dataset allowed an assessment of the measurement accuracy in terms of the methods’ recovery of expected mixing ratios”
The paper relies on this without support.
The SEQC samples prepared at different sites are comparable except for library preparation location.
“including libraries of the same samples that were prepared and sequenced at three different sites and libraries prepared at a separate site and only sequenced at the three sites”
The paper relies on this without support.
The ABRF samples prepared with different protocols are comparable except for the protocol.
“we downloaded a subset of samples from the ABRF Next-Generation Sequencing Study29 that were prepared using either ribosomal RNA depletion or poly-A selection”
The paper relies on this without support.
The IVT transcripts with high unpredictable coverage are representative of transcripts affected by fragment sequence bias.
“We focused our analysis on 64 of the IVT transcripts as exhibiting “high unpredictable coverage”17.”
The paper relies on this without support.
The poly-A selection protocol does not capture all non-coding transcripts.
“as the poly-A selection protocol is not designed to capture all the non-coding transcripts”
The paper relies on this without support.
The C and D samples in the ABRF dataset were made by mixing the A and B samples in 3:1 and 1:3 ratios, respectively.
“As the C and D samples in the ABRF dataset were created by mixing 3:1 and 1:3 ratios of the A and B samples respectively”
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If not corrected, these biases can cause high false positive rates in differential expression studies.
“When uncorrected, these biases can lead to, e.g., high false positive rates in differential expression studies [5].”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [5]
Existing transcriptome-wide abundance estimators, whether alignment-based or alignment-free, do not have sample-specific bias models detailed enough to capture major effects such as fragment GC content bias.
“However, existing methods for transcriptome-wide abundance estimation, both alignment-based and alignment-free, lack sample-specific bias models rich enough to capture important effects, like fragment GC content bias.”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [5]
The GC content of an entire fragment can strongly affect its likelihood of being selected for sequencing.
“it has also been observed that the GC-content of the entire fragment can play a substantial role in the likelihood that it will be selected for sequencing [5].”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [5]
The accuracy of isoform estimates is hindered by incomplete annotations, limitations of short-read sequencing, coverage, and genomic biases like GC content.
“the accuracy of these estimates is hindered by incomplete annotations, as well as by limitations of short-read sequencing, coverage, and genomic biases like GC content (84, 85).”
From Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018) · cites this paper as [85]
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179 · see citing works
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556 · see citing works
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700 · see citing works
Self-citationKim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668 · see citing works
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723 · see citing works
Software for Computing and Annotating Genomic Ranges
Lawrence, M. F., Huber, W. K., Pagès, H., Aboyoun, P., Carlson, M. R. J., Gentleman, R. C., Morgan, M. T., & Carey, V. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003118 · Cited by 4,284 · see citing works
Orchestrating high-throughput genomic analysis with Bioconductor
Huber, W. K., Carey, V. J., Gentleman, R. C., Anders, S., Carlson, M. R. J., Carvalho, B. S., Bravo, H. C., Davis, S. R., Gatto, L., Girke, T., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3252 · Cited by 3,499 · see citing works
Self-citationNormalization of RNA-seq data using factor analysis of control genes or samples
Risso, D., Ngai, J., Speed, T. P., & Dudoit, S. (2014) · Nature Biotechnology · doi:10.1038/nbt.2931 · Cited by 2,169 · see citing works
Transcriptome and genome sequencing uncovers functional variation in humans
The Geuvadis Consortium, Lappalainen, T., Sammeth, M., Friedländer, M. R., ‘t Hoen, P. A. C., Monlong, J., Rivas, M. A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., et al. (2013) · Nature · doi:10.1038/nature12531 · Cited by 1,888 · see citing works
Improving RNA-Seq expression estimates by correcting for fragment bias
Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L., & Pachter, L. (2011) · Genome Biology · doi:10.1186/gb-2011-12-3-r22 · Cited by 1,725 · see citing works
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Katz, Y., Wang, E. T., Airoldi, E. M., & Burge, C. B. (2010) · Nature Methods · doi:10.1038/nmeth.1528 · Cited by 1,353 · see citing works
RNA-Seq gene expression estimation with read mapping uncertainty
Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A., & Dewey, C. N. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp692 · Cited by 1,198 · see citing works
Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries
Aird, D., Ross, M. G., Chen, W.-S., Danielsson, M., Fennell, T., Russ, C., Jaffe, D. B., Nusbaum, C., & Gnirke, A. (2011) · Genome Biology · doi:10.1186/gb-2011-12-2-r18 · Cited by 1,174 · see citing works
Substantial biases in ultra-short read data sets from high-throughput DNA sequencing
Dohm, J. C., Lottaz, C., Borodina, T., & Himmelbauer, H. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn425 · Cited by 1,088 · see citing works
Stegle, O., Parts, L., Piipari, M., Winn, J., & Durbin, R. (2012) · Nature Protocols · doi:10.1038/nprot.2011.457 · Cited by 1,049 · see citing works
SEQC/MAQC-III Consortium (2014) · Nature Biotechnology · doi:10.1038/nbt.2957 · Cited by 983 · see citing works
Summarizing and correcting the GC content bias in high-throughput sequencing
Benjamini, Y., & Speed, T. P. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks001 · Cited by 913 · see citing works
Characterizing and measuring bias in sequence data
Ross, M. G., Russ, C., Costello, M., Hollinger, A., Lennon, N. J., Hegarty, R., Nusbaum, C., & Jaffe, D. B. (2013) · Genome Biology · doi:10.1186/gb-2013-14-5-r51 · Cited by 909 · see citing works
GC-Content Normalization for RNA-Seq Data
Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-480 · Cited by 867 · see citing works
Biases in Illumina transcriptome sequencing caused by random hexamer priming
Hansen, K. D., Brenner, S. E., & Dudoit, S. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq224 · Cited by 713 · see citing works
Removing technical variability in RNA-seq data using conditional quantile normalization
Hansen, K. D., Irizarry, R. A., & WU, Z. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr054 · Cited by 607 · see citing works
Self-citationPatro, R., Mount, S. M., & Kingsford, C. (2014) · Nature Biotechnology · doi:10.1038/nbt.2862 · Cited by 586 · see citing works
svaseq: removing batch effects and other unwanted noise from sequencing data
Leek, J. T. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku864 · Cited by 538 · see citing works
<i>Polyester</i> : simulating RNA-seq datasets with differential transcript expression
Frazee, A. C., Jaffe, A. E., Langmead, B., & Leek, J. T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv272 · Cited by 313 · see citing works
Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories
The GEUVADIS Consortium, 't Hoen, P. A. C., Friedländer, M. R., Almlöf, J., Sammeth, M., Pulyakhina, I., Anvar, S. Y., Laros, J. F. J., Buermans, H. P. J., Karlberg, O., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2702 · Cited by 250 · see citing works
Li, S., Tighe, S. W., Nicolet, C. M., Grove, D., Levy, S., Farmerie, W., Viale, A., Wright, C., Schweitzer, P. A., Gao, Y., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2972 · Cited by 240 · see citing works
Quantitative visualization of alternative exon expression from RNA-seq data
Yarden Katz, A., Wang, E. T., Silterra, J., Schwartz, S., Wong, B., Thorvaldsdóttir, H., Robinson, J. T., Mesirov, J. P., Airoldi, E. M., & Burge, C. B. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv034 · Cited by 220 · see citing works
Modeling non-uniformity in short-read rates in RNA-Seq data
Li, J., Jiang, H., & Wong, W. H. (2010) · Genome Biology · doi:10.1186/gb-2010-11-5-r50 · Cited by 193 · see citing works
Estimation of alternative splicing isoform frequencies from RNA-Seq data
Nicolae, M., Mangul, S., Măndoiu, I. I., & Zelikovsky, A. (2011) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-6-9 · Cited by 178 · see citing works
Detecting and correcting systematic variation in large-scale RNA sequencing data
Li, S., Łabaj, P. P., Zumbo, P., Sykacek, P., Shi, W., Shi, L., Phan, J., Wu, P.-Y., Wang, M., Wang, C., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3000 · Cited by 175 · see citing works
Bias detection and correction in RNA-Sequencing data
Zheng, W., Chung, L. M., & Zhao, H. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-290 · Cited by 161 · see citing works
IVT-seq reveals extreme bias in RNA sequencing
Lahens, N. F., Kavakli, I. H., Zhang, R., Hayer, K., Black, M. B., Dueck, H., Pizarro, A., Kim, J., Irizarry, R., Thomas, R. S., et al. (2014) · Genome Biology · doi:10.1186/gb-2014-15-6-r86 · Cited by 158 · see citing works
Self-citationLi, J. J., Jiang, C., Brown, J., Huang, H., & Bickel, P. J. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1113972108 · Cited by 142 · see citing works
Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads
Li, W., & Jiang, T. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts559 · Cited by 97 · see citing works
Hayer, K. E., Pizarro, A., Lahens, N. F., Hogenesch, J. B., & Grant, G. R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv488 · Cited by 93 · see citing works
Self-citationHron, T., Pajer, P., Pačes, J., Bartůněk, P., & Elleder, D. (2015) · Genome Biology · doi:10.1186/s13059-015-0724-z · Cited by 79 · see citing works
A penalized likelihood approach for robust estimation of isoform expression
Jiang, H., & Salzman, J. (2015) · Statistics and Its Interface · doi:10.4310/sii.2015.v8.n4.a3 · Cited by 15 · see citing works
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Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700 · see citing works
Self-citationDifferential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
Soneson, C., Love, M. I., & Robinson, M. D. (2015) · F1000Research · doi:10.12688/f1000research.7563.1 · Cited by 3,929 · see citing works
Self-citationDifferential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
Soneson, C., Love, M. I., & Robinson, M. D. (2016) · F1000Research · doi:10.12688/f1000research.7563.2 · Cited by 1,936 · see citing works
Self-citationTranscriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
Gandal, M. J., Zhang, P., Hadjimichael, E., Walker, R. L., Chen, C., Liu, S., Won, H., van Bakel, H., Varghese, M., Wang, Y., et al. (2018) · Science · doi:10.1126/science.aat8127 · Cited by 1,247 · see citing works
Voorwerk, L., Slagter, M., Horlings, H. M., Sikorska, K., de Vijver, K. K. V., de Maaker, M., Nederlof, I., Kluin, R. J. C., Warren, S., Ong, S., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0432-4 · Cited by 912 · see citing works
A general and flexible method for signal extraction from single-cell RNA-seq data
Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S., & Vert, J.-P. (2018) · Nature Communications · doi:10.1038/s41467-017-02554-5 · Cited by 718 · see citing works
Missing data and technical variability in single-cell RNA-sequencing experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxx053 · Cited by 635 · see citing works
Self-citationSterne-Weiler, T., Weatheritt, R. J., Best, A. J., Ha, K., & Blencowe, B. J. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.08.018 · Cited by 214 · see citing works
A benchmark for RNA-seq quantification pipelines
Teng, M., Love, M. I., Davis, C., Djebali, S., Dobin, A., Graveley, B. R., Li, S., Mason, C. E., Olson, S., Pervouchine, D. D., et al. (2016) · Genome biology · doi:10.1186/s13059-016-0940-1 · Cited by 189 · see citing works
Self-citationZrimec, J., Börlin, C. S., Buric, F., Muhammad, A. S., Chen, R., Siewers, V., Verendel, V., Nielsen, J., Töpel, M., & Zelezniak, A. (2020) · Nature Communications · doi:10.1038/s41467-020-19921-4 · Cited by 174 · see citing works
Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure
Zrimec, J., Buric, F., Muhammad, A. S., Chen, R., Verendel, V., Töpel, M., & Zelezniak, A. (2019) · doi:10.1101/792531 · Cited by 173 · see citing works
XenofilteR: computational deconvolution of mouse and human reads in tumor xenograft sequence data
Kluin, R. J. C., Kemper, K., Kuilman, T., de Ruiter, J. R., Iyer, V., Forment, J. V., Cornelissen‐Steijger, P., de Rink, I., ter Brugge, P., Song, J., et al. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2353-5 · Cited by 159 · see citing works
Mätlik, K., Baffuto, M., Kus, L., Deshmukh, A. L., Davis, D. A., Paul, M. R., Carroll, T. S., Caron, M., Masson, J., Pearson, C. E., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01653-6 · Cited by 151 · see citing works
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
den Berge, K. V., Hembach, K., Soneson, C., Tiberi, S., Clement, L., Love, M. I., Patro, R., & Robinson, M. D. (2019) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021255 · Cited by 144 · see citing works
Self-citationVan den Berge, K., Soneson, C., Robinson, M. D., & Clement, L. (2017) · Genome Biology · doi:10.1186/s13059-017-1277-0 · Cited by 126 · see citing works
Love, M. I., Soneson, C., & Patro, R. (2018) · F1000Research · doi:10.12688/f1000research.15398.1 · Cited by 123 · see citing works
Self-citationMeasuring and mitigating PCR bias in microbiota datasets
Silverman, J. D., Bloom, R. J., Jiang, S., Durand, H. K., Dallow, E. P., Mukherjee, S., & David, L. A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009113 · Cited by 115 · see citing works
Love, M. I., Soneson, C., & Patro, R. (2018) · F1000Research · doi:10.12688/f1000research.15398.3 · Cited by 112 · see citing works
Self-citationNonparametric expression analysis using inferential replicate counts
Zhu, A., Srivastava, A., Ibrahim, J. G., Patro, R., & Love, M. I. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz622 · Cited by 110 · see citing works
Self-citationPark, B., Hong, S., Valk, S. L., Paquola, C., Benkarim, O. M., Bethlehem, R. A. I., Martino, A. D., Milham, M. P., Gozzi, A., Yeo, B. T. T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21732-0 · Cited by 105 · see citing works
Computational approaches for interpreting sc<scp>RNA</scp>‐seq data
Rostom, R., Svensson, V., Teichmann, S. A., & Kar, G. (2017) · FEBS Letters · doi:10.1002/1873-3468.12684 · Cited by 107 · see citing works
Chen, Y., Davidson, N., Wan, Y. K., Yao, F., Su, Y., Gamaarachchi, H., Sim, A., Patel, H., Low, H. M., Hendra, C., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02623-4 · Cited by 191 · see citing works
Self-citationAddressing confounding artifacts in reconstruction of gene co-expression networks
Parsana, P., Ruberman, C., Jaffe, A. E., Schatz, M. C., Battle, A. J., & Leek, J. T. (2019) · Genome biology · doi:10.1186/s13059-019-1700-9 · Cited by 97 · see citing works
Selective vulnerability of layer 5a corticostriatal neurons in Huntington’s disease
Pressl, C., Mätlik, K., Kus, L., Darnell, P., Luo, J., Paul, M. R., Weiss, A. R., Liguore, W. A., Carroll, T. S., Davis, D. A., et al. (2024) · Neuron · doi:10.1016/j.neuron.2023.12.009 · Cited by 101 · see citing works
Molecular quantitative trait loci
Aguet, F., Alasoo, K., Li, Y. I., Battle, A., Im, H. K., Montgomery, S. B., & Lappalainen, T. (2023) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00188-6 · Cited by 98 · see citing works
Aicher, J. K., Jewell, P. D., Vaquero-Garcia, J., Barash, Y., & Bhoj, E. J. K. (2020) · Genetics in Medicine · doi:10.1038/s41436-020-0780-y · Cited by 94 · see citing works
Chen, Y., Davidson, N., Wan, Y. K., Patel, H., Yao, F., Low, H. M., Hendra, C., Watten, L., Sim, A. D., Sawyer, C., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.21.440736 · Cited by 92 · see citing works
Self-citationXia, C., Li, S., Hou, W., Fan, Z., Xiao, H., Lu, M., Sano, T., & Zhang, Z. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02427 · Cited by 79 · see citing works
Connectivity alterations in autism reflect functional idiosyncrasy
Benkarim, O. M., Paquola, C., Park, B., Hong, S., Royer, J., de Wael, R. V., Larivière, S., Valk, S. L., Bzdok, D., Mottron, L., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02572-6 · Cited by 81 · see citing works
Genetic effects on promoter usage are highly context-specific and contribute to complex traits
Alasoo, K., Rodrigues, J., Danesh, J., Freitag, D. F., Paul, D. S., & Gaffney, D. J. (2019) · eLife · doi:10.7554/elife.41673 · Cited by 63 · see citing works
Metagenomic Analysis Using Phylogenetic Placement—A Review of the First Decade
Czech, L., Stamatakis, A., Dunthorn, M., & Barbera, P. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.871393 · Cited by 62 · see citing works
Development of an improved inhibitor of Lats kinases to promote regeneration of mammalian organs
Kastan, N. R., Oak, S., Liang, R., Baxt, L. A., Myers, R. W., Ginn, J. D., Liverton, N. J., Huggins, D. J., Pichardo, J., Paul, M. R., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2206113119 · Cited by 63 · see citing works
Gates, L. A., Reis, B. S., Lund, P. J., Paul, M. R., Leboeuf, M., Djomo, A. M., Nadeem, Z., Lopes, M. D. C., Vitorino, F. N. D. L., Ünlü, G., et al. (2024) · Nature Metabolism · doi:10.1038/s42255-024-00992-2 · Cited by 67 · see citing works
Yang, Y., Yang, C., Han, S. J., Daldello, E. M., Cho, A., Martins, J. P. S., Xia, G., & Conti, M. (2017) · Genes & Development · doi:10.1101/gad.296871.117 · Cited by 61 · see citing works
García-Gómez, B. E., Ruiz, D., Salazar, J. A., Rubio, M., Martínez-Gómez, P., & Martínez-Gómez, P. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.01269 · Cited by 59 · see citing works
Current and Future Methods for mRNA Analysis: A Drive Toward Single Molecule Sequencing
Bayega, A., Fahiminiya, S., Oikonomopoulos, S., & Ragoussis, J. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7834-2_11 · Cited by 58 · see citing works
Deciphering highly similar multigene family transcripts from Iso-Seq data with IsoCon
Sahlin, K., Tomaszkiewicz, M., Makova, K., & Medvedev, P. (2018) · Nature Communications · doi:10.1038/s41467-018-06910-x · Cited by 51 · see citing works
Bioinformatics approaches for deciphering the epitranscriptome: Recent progress and emerging topics
Liu, L., Song, B., Ma, J., Song, Y., Zhang, S.-Y., Tang, Y., Wu, X., Wei, Z. C., Chen, K., Su, J., et al. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.010 · Cited by 56 · see citing works
Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2015) · doi:10.1101/025528 · Cited by 625 · see citing works
Self-citationComparative evaluation of full-length isoform quantification from RNA-Seq
Sarantopoulou, D., Brooks, T. G., Nayak, S., Mrčela, A., Lahens, N. F., & Grant, G. R. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04198-1 · Cited by 47 · see citing works
Marrocco, J., Gray, J. D., Kogan, J. F., Einhorn, N. R., O’Cinneide, E. M., Rubin, T. G., Carroll, T. S., Schmidt, E. F., & McEwen, B. S. (2019) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2019.00157 · Cited by 48 · see citing works
Novel roles for scleraxis in regulating adult tenocyte function
Nichols, A. E. C., Settlage, R. E., Werre, S. R., & Dahlgren, L. A. (2018) · BMC Cell Biology · doi:10.1186/s12860-018-0166-z · Cited by 48 · see citing works
de Araujo, L. S., Alves, M. R., Leal-Calvo, T., Leung, J., Durán, V., Samir, M., Talbot, S. R., Tallam, A., Mello, F. C. D. Q., Geffers, R., et al. (2019) · mBio · doi:10.1128/mbio.01037-19 · Cited by 45 · see citing works
Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq dataPreprint
Teng, M., & Irizarry, R. A. (2017) · Genome Research · doi:10.1101/gr.220673.117 · Cited by 40 · see citing works
Self-citationYu, M., Wang, M., Gyalpo, T., & Basang, Y. (2020) · Genomics · doi:10.1016/j.ygeno.2020.10.027 · Cited by 41 · see citing works
Liu, R., & Dickerson, J. A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005851 · Cited by 40 · see citing works
p-Value Histograms: Inference and Diagnostics
Breheny, P. J., Stromberg, A. J., & Lambert, J. (2018) · Microarrays · doi:10.3390/ht7030023 · Cited by 39 · see citing works
Buchholz, D. E., Carroll, T. S., Kocabas, A. M., Zhu, X., Behesti, H., Faust, P. L., Stalbow, L. A., Fang, Y., & Hatten, M. E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2000102117 · Cited by 38 · see citing works
Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers
Townes, F. W., & Irizarry, R. A. (2020) · Genome biology · doi:10.1186/s13059-020-02078-0 · Cited by 35 · see citing works
Self-citationNewman, J. R. B., Conesa, A., Mika, M., New, F. N., Önengüt-Gümüşcü, S., Atkinson, M. A., Rich, S. S., McIntyre, L. M., & CONCANNON, P. J. (2017) · Genome Research · doi:10.1101/gr.217984.116 · Cited by 33 · see citing works
Computational Methods for Mapping, Assembly and Quantification for Coding and Non-coding Transcripts
Babarinde, I. A., Li, Y., & Hutchins, A. P. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.04.012 · Cited by 30 · see citing works
Histone bivalency regulates the timing of cerebellar granule cell developmentPreprint
Mätlik, K., Govek, E., Paul, M. R., Allis, C. D., & Hatten, M. E. (2023) · Genes & Development · doi:10.1101/gad.350594.123 · Cited by 33 · see citing works
Liu, L., Xie, P., Li, W., Wu, Y., & An, W. (2018) · American Journal Of Pathology · doi:10.1016/j.ajpath.2018.11.006 · Cited by 29 · see citing works
Behesti, H., Kocabas, A. M., Buchholz, D. E., Carroll, T. S., & Hatten, M. E. (2021) · eLife · doi:10.7554/elife.67074 · Cited by 29 · see citing works
Placental multi-omics integration identifies candidate functional genes for birthweight
Ayele, F. T., Zeng, X., Chatterjee, S., Ouidir, M., Lesseur, C., Hao, K., Chen, J., Tesfaye, M., Marsit, C. J., Workalemahu, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30007-1 · Cited by 29 · see citing works
A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials
Wang, D., Liu, Y., Zhang, Y., Chen, Q., Han, Y., Hou, W., Liu, C., Yu, Y., Li, Z., Li, Z., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50420-y · Cited by 28 · see citing works
Chromatin remodeling factor BAZ1A regulates cellular senescence in both cancer and normal cells
Li, X., Ding, D., Yao, J., Zhou, B., Shen, T., Qi, Y., Ni, T., & Wei, G. (2019) · Life Sciences · doi:10.1016/j.lfs.2019.05.023 · Cited by 25 · see citing works
Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data
Sarkar, H., Srivastava, A., Bravo, H. C., Love, M. I., & Patro, R. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa448 · Cited by 21 · see citing works
Self-citationAlignment‐free methods for polyploid genomes: Quick and reliable genetic distance estimation
VanWallendael, A., & Álvarez, M. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13499 · Cited by 21 · see citing works
den Berge, K. V., Chou, H.-J., de Bézieux, H. R., Street, K., Risso, D., Ngai, J., & Dudoit, S. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100321 · Cited by 19 · see citing works
Correcting signal biases and detecting regulatory elements in STARR-seq dataPreprint
Kim, Y., Johnson, G. D., Seo, J., Barrera, A., Cowart, T., Majoros, W. H., Ochoa, A., Allen, A. S., & Reddy, T. E. (2021) · Genome Research · doi:10.1101/gr.269209.120 · Cited by 20 · see citing works
Soneson, C., Love, M. I., Patro, R., Hussain, S., Malhotra, D., & Robinson, M. D. (2019) · Life Science Alliance · doi:10.26508/lsa.201800175 · Cited by 19 · see citing works
Self-citationThe two enantiomers of 2-hydroxyglutarate differentially regulate cytotoxic T cell function
Foskolou, I. P., Cunha, P. P., Sánchez‐López, E., Minogue, E., Nicolet, B. P., Guislain, A., Jorgensen, C., Kostidis, S., Zandhuis, N. D., Barbieri, L., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113013 · Cited by 19 · see citing works
Recent advances in functional annotation and prediction of the epitranscriptome
Zhang, S.-Y., Zhang, S., Zhang, T., Fan, X.-N., & Meng, J. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.030 · Cited by 18 · see citing works
Corradin, O., Sallari, R. C., Hoang, A. T., Kassim, B. S., Hutta, G. B., Cuoto, L., Quach, B. C., Lovrenert, K., Hays, C., Gryder, B. E., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01477-y · Cited by 17 · see citing works
Gui, J., Wang, L., Liu, J., Luo, H., Huang, D., Yang, X., Song, H., Han, Z., Meng, L., Ding, R., et al. (2024) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2024.01.045 · Cited by 19 · see citing works
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Monod, A., Kališnik, S., Patiño-Galindo, J. Á., & Crawford, L. A. (2019) · SIAM Journal on Applied Algebra and Geometry · doi:10.1137/17m1148037 · Cited by 18 · see citing works
Barros, L. R. C., de Souza-Santos, P. T., Pretti, M. A. M., Vieira, G. F., Bragatte, M. A. D. S., Mendes, M. F. D. A., de Freitas, M. V., Scherer, N. D. M., de Oliveira, I. M., Rapozo, D. C. M., et al. (2020) · Journal of Leukocyte Biology · doi:10.1002/jlb.5ma0720-710rrr · Cited by 18 · see citing works
Ablation of Prdm16 and beige fat identity causes vascular remodeling and elevated blood pressure
Koenen, M., Becher, T., Pagano, G., Gaudio, I. D., Barrero, J. A., Montezano, A. C. I., Ortiz, J. R., Lin, Z., Gómez-Banoy, N., Amblard, R., et al. (2026) · Science · doi:10.1126/science.ady8644 · Cited by 19 · see citing works
Multi-omics approach to identifying isoform variants as therapeutic targets in cancer patients
Shaw, T. I., Zhao, B., Li, Y., Wang, H., Wang, L., Manley, B. J., Stewart, P. A., & Karolak, A. M. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1051487 · Cited by 16 · see citing works
Cilleros‐Portet, A., Lesseur, C., Marí, S., Cosín‐Tomás, M., Lozano, M., Irizar, A., Burt, A., García‐Santisteban, I., Garrido-Martín, D., Escaramís, G., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57760-3 · Cited by 18 · see citing works
ReSeq simulates realistic Illumina high-throughput sequencing data
Schmeing, S., & Robinson, M. D. (2021) · Genome biology · doi:10.1186/s13059-021-02265-7 · Cited by 17 · see citing works
DeRosa, H., Caradonna, S. G., Tran, H., Marrocco, J., & Kentner, A. C. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01744-y · Cited by 16 · see citing works
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Behera, S., Voshall, A., & Moriyama, E. N. (2021) · Exon Publications eBooks · doi:10.36255/exonpublications.bioinformatics.2021.ch7 · Cited by 14 · see citing works
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Davies, P., Jones, M., Liu, J., & Hebenstreit, D. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab148 · Cited by 14 · see citing works
Local sequence and sequencing depth dependent accuracy of RNA-seq reads
Cai, G., Liang, S., Zheng, X., & Xiao, F. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1780-z · Cited by 15 · see citing works
Mapping-based Genome Size EstimationPreprint
Natarajan, S., Gehrke, J., & Pucker, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/607390 · Cited by 14 · see citing works
AIDE: annotation-assisted isoform discovery with high precisionPreprint
Li, W. V., Li, S., Tong, X., Deng, L., Shi, H., & Li, J. J. (2019) · Genome Research · doi:10.1101/gr.251108.119 · Cited by 13 · see citing works
An allostatic epigenetic memory on chromatin footprints after double-hit acute stress
Caradonna, S. G., Paul, M. R., & Marrocco, J. (2022) · Neurobiology of Stress · doi:10.1016/j.ynstr.2022.100475 · Cited by 13 · see citing works
Choi, D., Kang, W., Park, S., Son, B., & Park, T. (2023) · Biomolecules · doi:10.3390/biom13060978 · Cited by 12 · see citing works
Mice lacking Astn2 have ASD-like behaviors and altered cerebellar circuit properties
Hanzel, M., Fernando, K., Maloney, S. E., Horn, Z., Gong, S., Mätlik, K., Zhao, J., Pasolli, H. A., Heissel, S., Dougherty, J. D., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2405901121 · Cited by 11 · see citing works
Improving gene isoform quantification with miniQuant
Li, H., Wang, D., Gao, Q., Tan, P., Wang, Y., Cai, X., Li, A., Zhao, Y., Thurman, A. L., Malekpour, S. A., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02633-9 · Cited by 12 · see citing works
Synthesizing genome regulation data with vote-counting
Fischer, M., & Hoffmann, S. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.06.012 · Cited by 12 · see citing works
Nacev, B. A., Dabas, Y., Paul, M. R., Pacheco, C., Mitchener, M. M., Perez, Y., Yan, F., Soshnev, A. A., Barrows, D., Carroll, T., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49486-5 · Cited by 12 · see citing works
O’Leary, H., Vanderlinden, L., Pantlin, L. N., Castano, A. M., Saba, L., & Benke, T. (2020) · Epilepsy Research · doi:10.1016/j.eplepsyres.2020.106283 · Cited by 10 · see citing works
SCUBE1 Controls BMPR2-Relevant Pulmonary Endothelial Function
Sun, W., Tang, Y. M., Tai, Y., Handen, A. L., Zhao, J., Speyer, G., Aaraj, Y. A., Watson, A. M. M., Romanelli, M., Sembrat, J., et al. (2020) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2020.08.010 · Cited by 11 · see citing works
Detecting, Categorizing, and Correcting Coverage Anomalies of RNA-Seq Quantification
Ma, C., & Kingsford, C. (2019) · Cell Systems · doi:10.1016/j.cels.2019.10.005 · Cited by 11 · see citing works
Xie, Z., Zhao, D., Li, B., Zeng, N., Wang, D., Wang, X., Sun, Y., Shao, M., Miao, S., Feng, F., et al. (2024) · LWT · doi:10.1016/j.lwt.2024.116389 · Cited by 10 · see citing works
Dai, L., Weiss, R. B., Dunn, D. M., Ramirez, A., Paul, S., & Korenberg, J. R. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab041 · Cited by 10 · see citing works
Transcriptomic analysis of paternal behaviors in prairie voles
Duclot, F., Liu, Y., Saland, S. K., Wang, Z., & Kabbaj, M. (2022) · BMC Genomics · doi:10.1186/s12864-022-08912-y · Cited by 10 · see citing works
Liu, Z., Fu, X., Xu, H., Zhang, Y., Shi, Z., Zhou, G., & Bao, W. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065652 · Cited by 10 · see citing works
Love, M. I., Soneson, C., & Patro, R. (2018) · F1000Research · doi:10.12688/f1000research.15398.2 · Cited by 10 · see citing works
Self-citationMeasuring and Mitigating PCR Bias in Microbiome DataPreprint
Silverman, J. D., Bloom, R. J., Jiang, S., Durand, H. K., Mukherjee, S., & David, L. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/604025 · Cited by 10 · see citing works
Smith, L. C., Абрамова, Е. В., Vayas, K. N., Rodriguez, J., Gelfand-Titiyevksiy, B., Roepke, T. A., Laskin, J. D., Gow, A. J., & Laskin, D. L. (2024) · Toxicological Sciences · doi:10.1093/toxsci/kfae081 · Cited by 9 · see citing works
Zhang, J., Venables, I., Callahan, D. L., Zwart, A. B., Passioura, J. B., Liu, Q., Zhou, X., Vanhercke, T., & Estavillo, G. M. (2025) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiaf151 · Cited by 9 · see citing works
DeRosa, H., Caradonna, S. G., Tran, H., Marrocco, J., & Kentner, A. C. (2022) · eNeuro · doi:10.1523/eneuro.0148-22.2022 · Cited by 10 · see citing works
Chabbert, C. D., Eberhart, T., Guccini, I., Krek, W., & Kovacs, W. J. (2019) · F1000Research · doi:10.12688/f1000research.17082.2 · Cited by 7 · see citing works
Kabir, T., Connamacher, C. A., Nadeem, Z., Paul, M. R., Smutny, M. R., Lawler, Z. K., Djomo, A. M., Carroll, T., & Gates, L. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.07.652677 · Cited by 8 · see citing works
BEERS2: RNA-Seq simulation through high fidelity in silico modeling
Brooks, T. G., Lahens, N. F., Mrčela, A., Sarantopoulou, D., Nayak, S., Naik, A. S., Sengupta, S., Choi, P. S., & Grant, G. R. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae164 · Cited by 7 · see citing works
- Authors
- Love, M. I., Hogenesch, J. B., & Irizarry, R. A.
- Affiliations
- Harvard University (US)
- University of Pennsylvania (US)
- Corresponding author
- Irizarry, Rafael A
- Venue
- Nature Biotechnology
- Published
- 2016-09-26
- Volume
- 34
- Issue
- 12
- Pages
- 1287-1291
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC5143225
- OpenAlex
- W1897931464
- Cited by
- 186
- References
- 39
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 15 · 2023: 9 · 2022: 38 · 2021: 29 · 2020: 18 · 2019: 20 · 2018: 19 · 2017: 10 · 2016: 4 · 2015: 1
- FWCI
- 9.18
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (NIH-R01, R01‐grant)
- Defense Advanced Research Projects Agency (DARPA-D12AP00025, D12AP00025)
- Advanced Research Projects Agency
- National Human Genome Research Institute (R01HG005220)
- National Cancer Institute (T32CA009337)
- National Institute of General Medical Sciences (R01GM083084)
- National Institute of Neurological Disorders and Stroke (R01NS054794)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq, transcript abundance estimation, GC content bias, Alpine, false positives
- MeSH terms
- Algorithms, Base Composition, Computer Simulation, Models, Genetic, RNA, Sensitivity and Specificity, Software, Transcription Factors, Reproducibility of Results, Models, Statistical, Artifacts, Sequence Analysis, RNA
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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