Streaming fragment assignment for real-time analysis of sequencing experiments
Open accessAbstract
We present eXpress, a software package for efficient probabilistic assignment of ambiguously mapping sequenced fragments. eXpress uses a streaming algorithm with linear run time and constant memory use. It can determine abundances of sequenced molecules in real time and can be applied to ChIP-seq, metagenomics and other large-scale sequencing data. We demonstrate its use on RNA-seq data and show that eXpress achieves greater efficiency than other quantification methods.
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Reads aligning with greater density at the 3′ end of transcripts and other confounders (for example, GC bias) caused false positives when classifying alternatively used splice sites based on predicted coverage.
“For example, reads aligning with greater density at the 3′ end of transcripts83,84 and other confounders (for example, GC bias) caused false positives as we attempted to classify alternatively used splice sites based on predicted coverage.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [84]
The paper’s main text (labeled Abstract in our copy; page unavailable) says sequence bias affects fragment prevalence at specific positions, supporting the general point that RNA-seq coverage can be uneven. It does not establish greater read density at the 3′ end of transcripts. The sentence presents the false-positive splice-site classifications as the citing authors’ own experience, not as a finding of this source. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe posterior probability that a read originated from one of the loci to which it aligns can be computed using an expectation-maximization (EM) algorithm.
“The posterior probability that a read originated from one of the loci to which it aligns can be computed using an expectation-maximization (EM) algorithm (Li et al. 2010; Nicolae et al. 2011; Roberts and Pachter 2013; Patro et al. 2014).”
From RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations (2014) · cites this paper as Roberts and Pachter 2013
Algorithms that quantify expression from transcriptome mappings allocate multi-mapping reads among transcripts and output within-sample normalized values corrected for sequencing biases.
“These methods allocate multi-mapping reads among transcript and output within-sample normalized values corrected for sequencing biases [35, 41, 43].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [41]
Sparse updates, where only transcripts appearing in a mini-batch are updated, allow efficient updates to the α parameters.
“These updates are sparse; i.e., only transcripts that appeared in some alignment in mini-batch Bτ will have their global weight updated after Bτ has been processed. This ensures, as in [15], that updates to the parameters α can be performed efficiently.”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [15]
With enough training data, one can learn a separate bin for each read position.
“given sufficient training data, it is even possible to learn a separate bin for each position of a read [15].”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [15]
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- Authors
- Roberts, A., & Pachter, L.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Pachter, Lior
- Venue
- Nature Methods
- Published
- 2013-01
- Volume
- 10
- Issue
- 1
- Pages
- 71-73
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC3880119
- OpenAlex
- W1990293955
- Cited by
- 1,063
- References
- 19
- Citations by year
- 2026: 37 · 2025: 50 · 2024: 47 · 2023: 68 · 2022: 102 · 2021: 106 · 2020: 101 · 2019: 88 · 2018: 106 · 2017: 105 · 2016: 83 · 2015: 83 · 2014: 64 · 2013: 21 · 2012: 1
- FWCI
- 21.72
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Human Genome Research Institute (R01HG006129)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- Express, streaming algorithms, RNA-seq quantification, ChIP-seq, metagenomics, linear-time algorithms
- MeSH terms
- Algorithms, Humans, RNA, Software, Sequence Analysis, DNA, Sequence Analysis, RNA, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Chromatin Immunoprecipitation, High-Throughput Nucleotide Sequencing
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