CoChat
Sign Up
Cited by 35

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

Banerjee, Sagnik, Bhandary, Priyanka, Woodhouse, Margaret, Sen, Taner Z., Wise, Roger P., Andorf, Carson M.·2021·BMC Bioinformatics
DOI 10.1186/s12859-021-04120-9PMC PMC8056616

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Background: Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative. Results: We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species. Conclusions: FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision—ideal for bench researchers with limited experience in handling computational tools.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12859-021-04120-9

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • FINDER uses RNA-seq assemblies with predicted open reading frames, in conjunction with BRAKER1 and homologous protein predictions.

    “FINDER follows an approach similar to BRAKER3. It uses RNA-seq assemblies with predicted open reading frames, in conjunction with BRAKER1 and homologous protein predictions.”

    From BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA (2024) · cites this paper as Banerjee et al. 2021

    The Implementation section says FINDER assembles RNA-Seq reads into transcripts, predicts their coding regions with GeneMarkS-T, and incorporates protein-derived gene models. It identifies the accompanying gene predictor as BRAKER2, not BRAKER1. (LLM, conf=0.92)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 166

  1. Genome List-Genome-NCBI. https://www.ncbi.nlm.nih.gov/genome/browse/#!/overview/. Accessed 12 Jan 2021.
  2. Morganti S, Tarantino P, Ferraro E, D’Amico P, Viale G, Trapani D, et al. Complexity of genome sequencing and reporting: next generation sequencing (NGS) technologies and implementation of precision medicine in real life. Crit Rev Oncol Hematol. 2019;133:171–82. · DOI
  3. Koboldt DC, Steinberg KM, Larson DE, Wilson RK, Mardis ER. The next-generation sequencing revolution and its impact on genomics. Cell. 2013;155:27–38. · DOI
  4. Phillips KA, Douglas MP. The global market for next-generation sequencing tests continues its torrid pace. J Precis Med. 2018;2018:4.
  5. Kulski JK. Next-generation sequencing—an overview of the history, tools, and “Omic” applications. Next Generation Sequencing–Advances, Applications and Challenges. 2016;3–60. · DOI
  6. Banerjee S, Mitra B, Chatterjee A, Santra A, Chatterjee B. Identification of relevant physico chemical properties of amino acids with respect to protein glycosylation prediction. In: Computing and Communication (IEMCON), 2015 International Conference and Workshop on. IEEE; 2015. p. 1–7. · DOI
  7. Banerjee S, Basu S, Nasipuri M. Big Data Analytics and Its Prospects in Computational Proteomics. In: Information systems design and intelligent applications. Springer; 2015. p. 591–8. · DOI
  8. Banerjee S, Velásquez-Zapata V, Fuerst G, Elmore JM, Wise RP, Elmore M. NGPINT: a next-generation protein–protein interaction software. Brief Bioinform. 2020;2020:1–14. https://doi.org/10.1093/bib/bbaa351. · DOI
  9. Rao VS, Srinivas K, Sujini GN, Kumar GN. Protein–protein interaction detection: methods and analysis. Int J Proteom. 2014;2014:147648. · DOI · Paper page
  10. Patel S, Tripathi R, Kumari V, Varadwaj P. DeepInteract: deep neural network based protein–protein interaction prediction tool. Curr Bioinform. 2017;12:551–7. · DOI
  11. Chen M, Ju CJ-T, Zhou G, Chen X, Zhang T, Chang K-W, et al. Multifaceted protein–protein interaction prediction based on siamese residual rcnn. Bioinformatics. 2019;35:305–14. · DOI
  12. Yang S, Li H, He H, Zhou Y, Zhang Z. Critical assessment and performance improvement of plant–pathogen protein–protein interaction prediction methods. Brief Bioinform. 2019;20:274–87. · DOI
  13. Li Y, Ilie L. SPRINT: ultrafast protein–protein interaction prediction of the entire human interactome. BMC Bioinform. 2017;18:485. · DOI
  14. Velásquez-Zapata V, Elmore JM, Banerjee S, Dorman KS, Wise RP. Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor. PLoS Comput Biol 2021. · DOI
  15. Banerjee S, Ghosh D, Basu S, Nasipuri M. JUPred_MLP: Prediction of phosphorylation sites using a consensus of MLP classifiers. 2016. · DOI
  16. Banerjee S, Ghosh D, Basu S, Nasipuri M. JUPred_SVM : Prediction of Phosphorylation Sites using a consensus of SVM classifiers. In: Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Springer; 2016. p. 1–8.
  17. Banerjee S, Nag S, Tapadar S, Ghosh S, Guha S, Bakshi S. Improving protein protein interaction prediction by choosing appropriate physiochemical properties of amino acids. In: Computing and Communication (IEMCON), 2015 International Conference and Workshop on. IEEE; 2015. p. 1–8. · DOI
  18. Banerjee S, Basu S, Ghosh D, Nasipuri M. PhospredRF: Prediction of protein phosphorylation sites using a consensus of random forest classifiers. In: Computing and Communication (IEMCON), 2015 International Conference and Workshop on. IEEE; 2015. p. 1–7. · DOI
  19. Luo F, Wang M, Liu Y, Zhao X-M, Li A. DeepPhos: prediction of protein phosphorylation sites with deep learning. Bioinformatics. 2019;35:2766–73. https://doi.org/10.1093/bioinformatics/bty1051. · DOI
  20. Li F, Li C, Marquez-Lago TT, Leier A, Akutsu T, Purcell AW, et al. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome. Bioinformatics. 2018;34:4223–31. · DOI
  21. Song J, Wang H, Wang J, Leier A, Marquez-Lago T, Yang B, et al. PhosphoPredict: a bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection. Sci Rep. 2017;7:1–19. · DOI · Paper page
  22. Chen H, Xue Y, Huang N, Yao X, Sun Z. MeMo: a web tool for prediction of protein methylation modifications. Nucl Acids Res. 2006;34 suppl_2:W249–53. · DOI
  23. Eisenhaber B, Eisenhaber F. Prediction of posttranslational modification of proteins from their amino acid sequence. In: Data mining techniques for the life sciences. Springer; 2010. p. 365–84. · DOI
  24. Elmore MG, Banerjee S, Pedley KF, Ruck A, Whitham SA. De novo transcriptome of Phakopsora pachyrhizi uncovers putative effector repertoire during infection. Physiol Mol Plant Pathol. 2020;110:101464. · DOI
  25. Frantzeskakis L, Kracher B, Kusch S, Yoshikawa-Maekawa M, Bauer S, Pedersen C, et al. Signatures of host specialization and a recent transposable element burst in the dynamic one-speed genome of the fungal barley powdery mildew pathogen. BMC Genomics. 2018;19:381. https://doi.org/10.1186/s12864-018-4750-6. · DOI
  26. Sperschneider J. Machine learning in plant–pathogen interactions: empowering biological predictions from field scale to genome scale. New Phytologist. 2019;nph.15771. https://doi.org/10.1111/nph.15771. · DOI
  27. Sperschneider J, Dodds PN, Singh KB, Taylor JM. ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning. New Phytologist. 2017. · DOI
  28. Sperschneider J, Gardiner DM, Dodds PN, Tini F, Covarelli L, Singh KB, et al. EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytol. 2016;210:743–61. https://doi.org/10.1111/nph.13794. · DOI
  29. Magnan CN, Baldi P. SSpro/ACCpro 5: Almost Perfect Prediction of Protein Secondary Structure and Relative Solvent Accessibility Using Profiles, Machine Learning, and Structural Similarity. Bioinformatics. 2014;:btu352. · DOI
  30. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics (Oxford, England). 2000;16:404–5.
  31. Banerjee S, Guha S, Dutta A, Dutta S. Improvement of protein disorder prediction by brainstorming consensus. In: Computing and Communication (IEMCON), 2015 International Conference and Workshop on. IEEE; 2015. p. 1–7. · DOI
  32. Salzberg SL. Next-generation genome annotation: we still struggle to get it right. BioMed Central; 2019. https://doi.org/10.1186/s13059-019-1715-2. · DOI · Paper page
  33. del Angel VD, Hjerde E, Sterck L, Capella-Gutierrez S, Notredame C, Pettersson OV, et al. Ten steps to get started in genome assembly and annotation. F1000Research. 2018;7. · DOI
  34. Richards S. Full disclosure: genome assembly is still hard. PLoS Biol. 2018;16:e2005894. · DOI
  35. Haas BJ, Delcher AL, Mount SM, Wortman JR, Smith RK Jr, Hannick LI, et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucl Acids Res. 2003;31:5654–66. · DOI
  36. Solovyev V, Kosarev P, Seledsov I, Vorobyev D. Automatic annotation of eukaryotic genes, pseudogenes and promoters. Genome Biol. 2006;7:S10. · DOI
  37. Kleffe J, Hermann K, Vahrson W, Wittig B, Brendel V. GeneGenerator—a flexible algorithm for gene prediction and its application to maize sequences. Bioinformatics (Oxford). 1998;14:232–43. · DOI
  38. Schweikert G, Zien A, Zeller G, Behr J, Dieterich C, Ong CS, et al. mGene: accurate SVM-based gene finding with an application to nematode genomes. Genome Res. 2009;19:2133–43. · DOI
  39. Schlueter SD, Dong Q, Brendel V. GeneSeqer@ PlantGDB: gene structure prediction in plant genomes. Nucl Acids Res. 2003;31:3597–600. · DOI
  40. Cantarel BL, Korf I, Robb SMCC, Parra G, Ross E, Moore B, et al. MAKER: An easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 2008;18:188–96. https://doi.org/10.1101/gr.6743907. · DOI
  41. Holt C, Yandell M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinform. 2011;12:491. · DOI · Paper page
  42. Campbell MS, Law M, Holt C, Stein JC, Moghe GD, Hufnagel DE, et al. MAKER-P: a tool kit for the rapid creation, management, and quality control of plant genome annotations. Plant Physiol. 2014;164:513–24. · DOI
  43. Campbell MS, Holt C, Moore B, Yandell M. Genome annotation and curation using MAKER and MAKER-P. Curr Protoc Bioinform. 2014;48:4–11. https://doi.org/10.1002/0471250953.bi0411s48. · DOI
  44. Vonk FJ, Casewell NR, Henkel CV, Heimberg AM, Jansen HJ, McCleary RJR, et al. The king cobra genome reveals dynamic gene evolution and adaptation in the snake venom system. Proc Natl Acad Sci. 2013;110:20651–6. · DOI
  45. Keane M, Semeiks J, Webb AE, Li YI, Quesada V, Craig T, et al. Insights into the evolution of longevity from the bowhead whale genome. Cell Rep. 2015;10:112–22. · DOI · Paper page
  46. Zhang J, Fu X-X, Li R-Q, Zhao X, Liu Y, Li M-H, et al. The hornwort genome and early land plant evolution. Nature plants. 2020;6:107–18. · DOI
  47. Gray MW, Burger G, Derelle R, Klimeš V, Leger MM, Sarrasin M, et al. The draft nuclear genome sequence and predicted mitochondrial proteome of Andalucia godoyi, a protist with the most gene-rich and bacteria-like mitochondrial genome. BMC Biol. 2020;18:1–35. · DOI
  48. Peng C, Ren J-L, Deng C, Jiang D, Wang J, Qu J, et al. The genome of Shaw’s sea snake (Hydrophis curtus) reveals secondary adaptation to its marine environment. Mol Biol Evol; 2020. · DOI
  49. Weitemier K, Straub SCK, Fishbein M, Bailey CD, Cronn RC, Liston A. A draft genome and transcriptome of common milkweed (Asclepias syriaca) as resources for evolutionary, ecological, and molecular studies in milkweeds and Apocynaceae. PeerJ. 2019;7:e7649. · DOI
  50. Zhang J, Zhang X, Tang H, Zhang Q, Hua X, Ma X, et al. Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L. Nat Genet. 2018;50:1565–73. · DOI
  51. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18:821–9. · DOI
  52. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat Biotechnol. 2011;29:644. · DOI
  53. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52. · DOI
  54. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77. · DOI
  55. Xie Y, Wu G, Tang J, Luo R, Patterson J, Liu S, et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014;30:1660–6. · DOI
  56. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511. · DOI · Paper page
  57. Pertea M, Pertea GM, Antonescu CM, Chang T-C, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290–5. https://doi.org/10.1038/nbt.3122. · DOI
  58. Liu R, Dickerson J. Strawberry: fast and accurate genome-guided transcript reconstruction and quantification from RNA-Seq. PLoS Comput Biol. 2017;13:e1005851. · DOI
  59. Shao M, Kingsford C. Accurate assembly of transcripts through phase-preserving graph decomposition. Nat Biotechnol. 2017;35:1167–9. https://doi.org/10.1038/nbt.4020. · DOI
  60. Kovaka S, Zimin AV, Pertea GM, Razaghi R, Salzberg SL, Pertea M. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 2019;20:1–13. · DOI
  61. Song L, Sabunciyan S, Yang G, Florea L. A multi-sample approach increases the accuracy of transcript assembly. Nat Commun. 2019;10:5000. https://doi.org/10.1038/s41467-019-12990-0. · DOI
  62. Hoff KJ, Lange S, Lomsadze A, Borodovsky M, Stanke M. BRAKER1: unsupervised RNA-Seq-based genome annotation with GeneMark-ET and AUGUSTUS: Table 1. Bioinformatics. 2016;32:767–9. https://doi.org/10.1093/bioinformatics/btv661. · DOI
  63. Hoff KJ, Lomsadze A, Borodovsky M, Stanke M. Whole-genome annotation with BRAKER. In: Gene prediction. Springer; 2019. p. 65–95. · DOI
  64. Steijger T, Abril JF, Engström PG, Kokocinski F, Akerman M, Alioto T, et al. Assessment of transcript reconstruction methods for RNA-seq. Nat Methods. 2013;10:1177–84. · DOI · Paper page
  65. Lomsadze A, Burns PD, Borodovsky M. Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm. Nucl Acids Res. 2014;42:e119–e119. https://doi.org/10.1093/nar/gku557. · DOI · Paper page
  66. Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008;24:637–44. · DOI · Paper page
  67. Korf I. Gene finding in novel genomes. BMC Bioinform. 2004;5:59. · DOI
  68. Keilwagen J, Hartung F, Grau J. GeMoMa: Homology-based gene prediction utilizing intron position conservation and RNA-seq data. In: Methods in molecular biology. 2019. · DOI
  69. Leinonen R, Sugawara H, Shumway M, Collaboration INSD. The sequence read archive. Nucl Acids Res. 2010;39 suppl_1:D19–21. · DOI
  70. Dobin A, Gingeras TR, Spring C, Flores R, Sampson J, Knight R, et al. Mapping RNA-seq with STAR. Curr Protoc Bioinform. 2016;51:586–97.
  71. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. · DOI · Paper page
  72. Tang S, Lomsadze A, Borodovsky M. Identification of protein coding regions in RNA transcripts. Nucleic Acids Res. 2015;43:e78. https://doi.org/10.1093/nar/gkv227. · DOI
  73. Goodstadt L. Ruffus: a lightweight Python library for computational pipelines. Bioinformatics. 2010;26:2778–9. · DOI
  74. Engström PG, Steijger T, Sipos B, Grant GR, Kahles A, Alioto T, et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods. 2013;10:1185–91. · DOI · Paper page
  75. Rapazote-Flores P, Bayer M, Milne L, Mayer C-D, Fuller J, Guo W, et al. BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq. BMC Genomics. 2019;20:1–17. · DOI
  76. Ustianenko D, Weyn-Vanhentenryck SM, Zhang C. Microexons: discovery, regulation, and function. Wiley Interdiscip Rev RNA. 2017;8:e1418. · DOI
  77. Curry-Hyde A, Chen BJ, Mills JD, Janitz M. Microexons: novel regulators of the transcriptome. J Hum Transcript. 2018;2:1–6. · DOI
  78. Wen F, Li F, Xia H, Lu X, Zhang X, Li Y. The impact of very short alternative splicing on protein structures and functions in the human genome. Trends Genet. 2004;20:232–6. · DOI
  79. Sakharkar MK, Chow VTK, Kangueane P. Distributions of exons and introns in the human genome. silico biology. 2004;4:387–93.
  80. Mano F, Aoyanagi T, Kozaki A. Atypical splicing accompanied by skipping conserved micro-exons produces unique WRINKLED1, an AP2 domain transcription factor in rice plants. Plants. 2019;8:207. · DOI
  81. Song Q, Lv F, Tahir ul Qamar M, Xing F, Zhou R, Li H, et al. Identification and analysis of micro-exon genes in the rice genome. Int J Mol Sci. 2019;20:2685. · DOI
  82. Bulman S, Ridgway HJ, Eady C, Conner AJ. Intron-rich gene structure in the intracellular plant parasite Plasmodiophora brassicae. Protist. 2007;158:423–33. · DOI
  83. Wang X. Protein and proteome atlas for plants under stresses: new highlights and ways for integrated Omics in post-genomics era; 2019. · DOI
  84. Guo L, Liu C-M. A single-nucleotide exon found in Arabidopsis. Sci Rep. 2015;5:18087. · DOI
  85. Gonatopoulos-Pournatzis T, Wu M, Braunschweig U, Roth J, Han H, Best AJ, et al. Genome-wide CRISPR-Cas9 interrogation of splicing networks reveals a mechanism for recognition of autism-misregulated neuronal microexons. Mol Cell. 2018;72:510–24. · DOI
  86. Torres-Méndez A, Bonnal S, Marquez Y, Roth J, Iglesias M, Permanyer J, et al. A novel protein domain in an ancestral splicing factor drove the evolution of neural microexons. Nat Ecol Evol. 2019;3:691–701. · DOI
  87. Parras A, Anta H, Santos-Galindo M, Swarup V, Elorza A, Nieto-González JL, et al. Autism-like phenotype and risk gene mRNA deadenylation by CPEB4 mis-splicing. Nature. 2018;560:441–6. · DOI
  88. Wu J, Anczukow O, Krainer AR, Zhang MQ, Zhang C. OLego: fast and sensitive mapping of spliced mRNA-Seq reads using small seeds. Nucleic Acids Res. 2013;41:5149–63. · DOI · Paper page
  89. Kawahara Y, Sugiyama M. Change-point detection in time-series data by direct density-ratio estimation. In: Proceedings of the 2009 SIAM International Conference on Data Mining. SIAM; 2009. p. 389–400. · DOI
  90. Lund R, Wang XL, Lu QQ, Reeves J, Gallagher C, Feng Y. Changepoint detection in periodic and autocorrelated time series. J Clim. 2007;20:5178–90. · DOI
  91. Kawahara Y, Yairi T, Machida K. Change-point detection in time-series data based on subspace identification. In: Seventh IEEE international conference on data mining (ICDM 2007). IEEE; 2007. p. 559–64. · DOI
  92. Takeuchi J, Yamanishi K. A unifying framework for detecting outliers and change points from time series. IEEE Trans Knowl Data Eng. 2006;18:482–92. · DOI
  93. Tartakovsky AG, Rozovskii BL, Blazek RB, Kim H. A novel approach to detection of intrusions in computer networks via adaptive sequential and batch-sequential change-point detection methods. IEEE Trans Signal Process. 2006;54:3372–82. · DOI
  94. Klanderman MC, Newhart KB, Cath TY, Hering AS. Fault isolation for a complex decentralized waste water treatment facility. J R Stat Soc Ser C. 2020;69:931–51. · DOI
  95. Quinlan AR. BEDTools: the Swiss-army tool for genome feature analysis. Curr Protoc Bioinformatics. 2014;47:11–2. · DOI
  96. Xiang S, Huang Z, Wang T, Han Z, Christina YY, Ni D, et al. Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients. BMC Med Genomics. 2018;11:115. · DOI
  97. Bruna T, Hoff K, Stanke M, Lomsadze A, Borodovsky M. BRAKER2: Automatic Eukaryotic Genome Annotation with GeneMark-EP+ and AUGUSTUS Supported by a Protein Database. bioRxiv. 2020. · DOI
  98. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10. · DOI · Paper page
  99. Slater GSC, Birney E. Automated generation of heuristics for biological sequence comparison. BMC Bioinform. 2005;6:31. · DOI
  100. Cheng C, Krishnakumar V, Chan AP, Thibaud-Nissen F, Schobel S, Town CD. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome. Plant J. 2017;89:789–804. · DOI
  101. Li J-Y, Wang J, Zeigler RS. The 3,000 rice genomes project: new opportunities and challenges for future rice research. Gigascience. 2014;3:2047–217. · DOI
  102. Duitama J, Silva A, Sanabria Y, Cruz DF, Quintero C, Ballen C, et al. Whole genome sequencing of elite rice cultivars as a comprehensive information resource for marker assisted selection. PLoS ONE. 2015;10:e0124617. · DOI
  103. Zhao K, Wright M, Kimball J, Eizenga G, McClung A, Kovach M, et al. Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome. PLoS ONE. 2010;5:e10780. · DOI
  104. Wang B, Tseng E, Regulski M, Clark TA, Hon T, Jiao Y, et al. Unveiling the complexity of the maize transcriptome by single-molecule long-read sequencing. Nat Commun. 2016;7:11708. https://doi.org/10.1038/ncomms11708. · DOI
  105. Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, et al. The B73 maize genome: complexity, diversity, and dynamics. Science. 2009;326:1112–5. · DOI
  106. The_C_elegans_Sequencing_Consortium. Genome sequence of the nematode C. elegans: a platform for investigating biology. Science. 1998;282:2012–8. · DOI
  107. Ter-Hovhannisyan V, Lomsadze A, Chernoff YO, Borodovsky M. Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training. Genome Res. 2008;18:1979–90. https://doi.org/10.1101/gr.081612.108. · DOI
  108. International_Human_Genome_Sequencing_consortium. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921.
  109. Hood L, Rowen L. The human genome project: big science transforms biology and medicine. Genome Med. 2013;5:79. · DOI
  110. Monat C, Padmarasu S, Lux T, Wicker T, Gundlach H, Himmelbach A, et al. TRITEX: chromosome-scale sequence assembly of Triticeae genomes with open-source tools. Genome Biol. 2019;20:284. · DOI · Paper page
  111. Krasileva KV, Vasquez-Gross HA, Howell T, Bailey P, Paraiso F, Clissold L, et al. Uncovering hidden variation in polyploid wheat. Proc Natl Acad Sci. 2017;114:E913–21. · DOI
  112. Clavijo BJ, Venturini L, Schudoma C, Accinelli GG, Kaithakottil G, Wright J, et al. An improved assembly and annotation of the allohexaploid wheat genome identifies complete families of agronomic genes and provides genomic evidence for chromosomal translocations. Genome Res. 2017;27:885–96. · DOI
  113. Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, et al. RefSeq: an update on mammalian reference sequences. Nucl Acids Res. 2014;42:D756–63. · DOI
  114. Tello-Ruiz MK, Naithani S, Stein JC, Gupta P, Campbell M, Olson A, et al. Gramene 2018: unifying comparative genomics and pathway resources for plant research. Nucl Acids Res. 2018;46:D1181–9. · DOI
  115. Jiao Y, Peluso P, Shi J, Liang T, Stitzer MC, Wang B, et al. Improved maize reference genome with single-molecule technologies. Nature. 2017;546:524–7. · DOI
  116. Eilbeck K, Moore B, Holt C, Yandell M. Quantitative measures for the management and comparison of annotated genomes. BMC Bioinform. 2009;10:67. · DOI
  117. Venturini L, Caim S, Kaithakottil GG, Mapleson DL, Swarbreck D. Leveraging multiple transcriptome assembly methods for improved gene structure annotation. GigaScience. 2018;7. https://doi.org/10.1093/gigascience/giy093. · DOI
  118. Sreenivasamurthy SK, Madugundu AK, Patil AH, Dey G, Mohanty AK, Kumar M, et al. Mosquito-borne diseases and Omics: tissue-restricted expression and alternative splicing revealed by transcriptome profiling of Anopheles stephensi. Omics J Integr Biol. 2017;21:488–97. · DOI
  119. Azlan A, Obeidat SM, Yunus MA, Azzam G. Transcriptome profiles and novel lncRNA identification of Aedes aegypti cells in response to dengue virus serotype 1. BioRxiv. 2018;:422170.
  120. Azlan A, Halim MA, Azzam G. Genome-wide identification and characterization of long intergenic noncoding RNAs in the regenerative flatworm Macrostomum lignano. Genomics. 2020;112:1273–81. · DOI
  121. Qi S, Akter S, Li S. Identification of Novel lincRNA and Co-Expression Network Analysis Using RNA-Sequencing Data in Plants. In: Plant long non-coding RNAs. Springer; 2019. p. 207–21. · DOI
  122. Beisel NS, Noble J, Barbazuk WB, Paul A-L, Ferl RJ. Spaceflight-induced alternative splicing during seedling development in Arabidopsis thaliana. NPJ Micrograv. 2019;5:1–5. · DOI
  123. Wang C, Wallerman O, Arendt M-L, Sundstrom E, Karlsson A, Nordin J, et al. A new long-read dog assembly uncovers thousands of exons and functional elements missing in the previous reference. bioRxiv. 2020. · DOI
  124. Liu S, Aagaard A, Bechsgaard J, Bilde T. DNA methylation patterns in the social spider. Stegodyphus dumicola Genes. 2019;10:137. · DOI
  125. Wu S, Gao S, Wang S, Meng J, Wickham J, Luo S, et al. A reference genome of bursaphelenchus mucronatus provides new resources for revealing its displacement by pinewood nematode. Genes. 2020;11:570. · DOI
  126. Wang P, Luo Y, Huang J, Gao S, Zhu G, Dang Z, et al. The genome evolution and domestication of tropical fruit mango. Genome Biol. 2020;21:1–17. · DOI
  127. Cieślik M, Chinnaiyan AM. Cancer transcriptome profiling at the juncture of clinical translation. Nat Rev Genet. 2018;19:93. · DOI
  128. Lorenzi L, Avila Cobos F, Decock A, Everaert C, Helsmoortel H, Lefever S, et al. Long noncoding RNA expression profiling in cancer: challenges and opportunities. Genes Chromosom Cancer. 2019;58:191–9. · DOI
  129. Yang J, Moeinzadeh M-H, Kuhl H, Helmuth J, Xiao P, Haas S, et al. Haplotype-resolved sweet potato genome traces back its hexaploidization history. Nature plants. 2017;3:696–703. · DOI
  130. Sun Z, Nair A, Chen X, Prodduturi N, Wang J, Kocher J-P. UClncR: ultrafast and comprehensive long non-coding RNA detection from RNA-seq. Sci Rep. 2017;7:1–10. · DOI · Paper page
  131. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7:562–78. · DOI
  132. Arrigoni A, Ranzani V, Rossetti G, Panzeri I, Abrignani S, Bonnal RJP, et al. Analysis RNA-seq and Noncoding RNA. In: Polycomb group proteins. Springer; 2016. p. 125–35. · DOI
  133. Ghosh S, Chan C-KK. Analysis of RNA-Seq data using TopHat and Cufflinks. In: Plant Bioinformatics. Springer; 2016. p. 339–61. · DOI
  134. Qi X, Xie S, Liu Y, Yi F, Yu J. Genome-wide annotation of genes and noncoding RNAs of foxtail millet in response to simulated drought stress by deep sequencing. Plant Mol Biol. 2013;83:459–73. · DOI
  135. Marchant A, Mougel F, Mendonça V, Quartier M, Jacquin-Joly E, da Rosa JA, et al. Comparing de novo and reference-based transcriptome assembly strategies by applying them to the blood-sucking bug Rhodnius prolixus. Insect Biochem Mol Biol. 2016;69:25–33. · DOI
  136. Li A, Zhang J, Zhou Z, Wang L, Liu Y, Liu Y. ALDB: a domestic-animal long noncoding RNA database. PLoS ONE. 2015;10:e0124003. · DOI
  137. Cooper SJ, Trinklein ND, Anton ED, Nguyen L, Myers RM. Comprehensive analysis of transcriptional promoter structure and function in 1% of the human genome. Genome Res. 2006;16:1–10. · DOI
  138. Brown RH, Gross SS, Brent MR. Begin at the beginning: predicting genes with 5′ UTRs. Genome Res. 2005;15:742–7. · DOI
  139. Ohler U, Liao G, Niemann H, Rubin GM. Computational analysis of core promoters in the Drosophila genome. Genome Biol. 2002;3:research0087–1. · DOI
  140. Batut P, Gingeras TR. RAMPAGE: promoter activity profiling by paired-end sequencing of 5′-complete cDNAs. Curr Protoc Mol Biol. 2013;104:25B-B11. · DOI
  141. Adiconis X, Haber AL, Simmons SK, Levy Moonshine A, Ji Z, Busby MA, et al. Comprehensive comparative analysis of 5′-end RNA-sequencing methods. Nat Methods. 2018;15:505–11. · DOI
  142. Shiraki T, Kondo S, Katayama S, Waki K, Kasukawa T, Kawaji H, et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc Natl Acad Sci. 2003;100:15776–81. · DOI
  143. Holmqvist E, Wright PR, Li L, Bischler T, Barquist L, Reinhardt R, et al. Global RNA recognition patterns of post-transcriptional regulators Hfq and CsrA revealed by UV crosslinking in vivo. EMBO J. 2016;35:991–1011. · DOI
  144. Hickman R, van Verk MC, van Dijken AJH, Mendes MP, Vroegop-Vos IA, Caarls L, et al. Architecture and dynamics of the jasmonic acid gene regulatory network. Plant Cell Online. 2017;:tpc-00958. · DOI
  145. Jackson RJ, Standart N. Do the poly (A) tail and 3′ untranslated region control mRNA translation? Cell. 1990;62:15–24. · DOI · Paper page
  146. Meijer HA, Thomas AAM. Control of eukaryotic protein synthesis by upstream open reading frames in the 5′-untranslated region of an mRNA. Biochem J. 2002;367:1–11. · DOI
  147. Miller GM, Madras BK. Polymorphisms in the 3′-untranslated region of human and monkey dopamine transporter genes affect reporter gene expression. Mol Psychiatry. 2002;7:44–55. · DOI
  148. Wu S, Huang S, Ding J, Zhao Y, Liang L, Liu T, et al. Multiple microRNAs modulate p21Cip1/Waf1 expression by directly targeting its 3′ untranslated region. Oncogene. 2010;29:2302–8. · DOI · Paper page
  149. Dixon DA, Kaplan CD, McIntyre TM, Zimmerman GA, Prescott SM. Post-transcriptional control of cyclooxygenase-2 gene expression The role of the 3′-untranslated region. J Biol Chem. 2000;275:11750–7. · DOI
  150. Gu S, Jin L, Zhang F, Sarnow P, Kay MA. Biological basis for restriction of microRNA targets to the 3′ untranslated region in mammalian mRNAs. Nat Struct Mol Biol. 2009;16:144. · DOI
  151. Eberle AB, Stalder L, Mathys H, Orozco RZ, Mühlemann O. Posttranscriptional gene regulation by spatial rearrangement of the 3′ untranslated region. PLoS Biol. 2008;6:e92. · DOI
  152. Halterman DA, Wise RP. Upstream open reading frames of the barley Mla13 powdery mildew resistance gene function co-operatively to down-regulate translation. Mol Plant Pathol. 2006;7:167–76. · DOI
  153. Awata T, Inoue K, Kurihara S, Ohkubo T, Watanabe M, Inukai K, et al. A common polymorphism in the 5′-untranslated region of the VEGF gene is associated with diabetic retinopathy in type 2 diabetes. Diabetes. 2002;51:1635–9. · DOI
  154. Rogers JT, Randall JD, Cahill CM, Eder PS, Huang X, Gunshin H, et al. An iron-responsive element type II in the 5′-untranslated region of the Alzheimer’s amyloid precursor protein transcript. J Biol Chem. 2002;277:45518–28. · DOI · Paper page
  155. Chin LJ, Ratner E, Leng S, Zhai R, Nallur S, Babar I, et al. A SNP in a let-7 microRNA complementary site in the KRAS 3′ untranslated region increases non-small cell lung cancer risk. Can Res. 2008;68:8535–40. · DOI
  156. Halterman DA, Wei F, Wise RP. Powdery mildew-induced Mla mRNAs are alternatively spliced and contain multiple upstream open reading frames. Plant Physiol. 2003;131:558–67. https://doi.org/10.1104/pp.014407. · DOI
  157. Conne B, Stutz A, Vassalli J-D. The 3′ untranslated region of messenger RNA: a molecular ‘hotspot’for pathology? Nat Med. 2000;6:637–41. · DOI
  158. Hunt M, Banerjee S, Surana P, Liu M, Fuerst G, Mathioni S, et al. Small RNA discovery in the interaction between barley and the powdery mildew pathogen. BMC Genomics. 2019;20:610. · DOI
  159. Chapman AVE, Matthew H, Surana P, Velásquez-Zapata V, Xu W, Fuerst G, et al. Disruption of barley immunity to powdery mildew by an in-frame Lys-Leu deletion in the essential protein SGT1. Oxford Genetics. 2020. · DOI
  160. Dai X, Xu Z, Liang Z, Tu X, Zhong S, Schnable JC. Non-homology-based prediction of gene functions. 2019;1–18. · DOI
  161. Duvick DN. The contribution of breeding to yield advances in maize (Zea mays L.). Adv Agronomy. 2005;86:83–145. · DOI · Paper page
  162. Agrama HAS, Moussa ME. Mapping QTLs in breeding for drought tolerance in maize (Zea mays L.). Euphytica. 1996;91:89–97. · DOI
  163. Maazou A-RS, Tu J, Qiu J, Liu Z. Breeding for drought tolerance in maize (Zea mays L.). Am J Plant Sci. 2016;7:1858.
  164. Bhandary P, Seetharam AS, Arendsee ZW, Hur M, Wurtele ES. Raising orphans from a metadata morass: a researcher’s guide to re-use of public ’omics data. Plant Sci. 2018. https://doi.org/10.1016/j.plantsci.2017.10.014. · DOI
  165. Arendsee ZW, Li L, Wurtele ES. Coming of age: orphan genes in plants. Trends Plant Sci. 2014;19:698–708. https://doi.org/10.1016/J.TPLANTS.2014.07.003. · DOI
  166. Arendsee Z, Li J, Singh U, Seetharam A, Dorman K, Wurtele ES. phylostratr: a framework for phylostratigraphy. Bioinformatics. 2019;35:3617–27. · DOI

Cited by

Sorted by citations

Metadata

Authors
Banerjee, S., Bhandary, P., Woodhouse, M., Sen, T. Z., Wise, R. P., & Andorf, C. M.
ORCID
Andorf, Carson M.
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2021-04-20
Volume
22
Issue
1
ISSN
1471-2105
DOI
10.1186/s12859-021-04120-9
PMC
PMC8056616
Cited by
35
References
7
Language
en
Licence
Funders
  • USDA-ARS (5030-21000-068-00D, 3625-21000-067-00D, 2030-21000-024-00D)
  • National Science Foundation (1546858)

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review