The quantification of RNA sequencing (RNA-seq) abundance using a normalization method that calculates transcripts per million (TPM) is a key step to compare multiple samples from different experiments. TPMCalculator is a one-step software to process RNA-seq alignments in BAM format and reports TPM values, raw read counts and feature lengths for genes, transcripts, exons and introns. The program describes the genomic features through a model generated from the gene transfer format file used during alignments reporting of the TPM values and the raw read counts for each feature. In this paper, we show the correlation for 1256 samples from the TCGA-BRCA project between TPM and FPKM reported by TPMCalculator and RSeQC. We also show the correlation for raw read counts reported by TPMCalculator, HTSeq and featureCounts.TPMCalculator is freely available at https://github.com/ncbi/TPMCalculator. It is implemented in C++14 and supported on Mac OS X, Linux and MS Windows.Supplementary data are available at Bioinformatics online

Landsman, David

Mariño-Ramírez, Leonardo

Pongor, Lorinc Sandor

Vera Alvarez, Roberto

Semmelweis Repository

Genome analysisTPMCalculator: one-step software to quantifymRNA abundance of genomic featuresRoberto Vera Alvarez1,*, Lorinc Sandor Pongor1,2,Leonardo Mari~no-Ramı́rez1 and David Landsman11Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine,National Institutes of Health, Bethesda, MD 20892, USA and 22nd Department of Pediatrics, SemmelweisUniversity, Budapest 1094, Hungary*To whom correspondence should be addressed.Associate Editor: Bonnie BergerReceived on March 13, 2018; revised on October 2, 2018; editorial decision on October 22, 2018; accepted on October 30, 2018AbstractSummary: The quantification of RNA sequencing (RNA-seq) abundance using a normalizationmethod that calculates transcripts per million (TPM) is a key step to compare multiple samplesfrom different experiments. TPMCalculator is a one-step software to process RNA-seq alignmentsin BAM format and reports TPM values, raw read counts and feature lengths for genes, transcripts,exons and introns. The program describes the genomic features through a model generated fromthe gene transfer format file used during alignments reporting of the TPM values and the raw readcounts for each feature. In this paper, we show the correlation for 1256 samples from the TCGA-BRCA project between TPM and FPKM reported by TPMCalculator and RSeQC. We also show thecorrelation for raw read counts reported by TPMCalculator, HTSeq and featureCounts.Availability and implementation: TPMCalculator is freely available at https://github.com/ncbi/TPMCalculator. It is implemented in Cþþ14 and supported on Mac OS X, Linux and MS Windows.Contact: veraalva@ncbi.nlm.nih.govSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionNext-Generation Sequencing technologies are changing the way weanalyze biological systems. RNA sequencing (RNA-seq) has becomea standard procedure. Most RNA-seq experiments measure andcompare transcript abundance between samples, which is a criticalstep for analyzing gene expression profiles under varying experimen-tal conditions.In 2008, Mortazavi et al. introduced a normalization methoddesigned to measure mRNA abundance and named the measure‘reads per kilobase of exon model per million mapped reads’(RPKM) (Mortazavi et al., 2008). This was later modified byTrapnell et al. by an alternative method named ‘fragments per kilo-base of transcript per million fragments sequenced’ (FPKM)(Trapnell et al., 2010). Although both RPKM and FPKM offer prac-tical ways to quantify mRNA abundance while comparing genomicfeatures in the same sample, they may present biased values whencomparing multiple samples (Wagner et al., 2012). Due to thisinconsistence, and the fact that RNA-Seq data analysis is more use-ful when comparing multiple samples from different experimentalconditions, Wagner et al. introduced an alternative quantity toRPKM and FPKM named ‘transcripts per million’ (TPM) that cor-rects the inconsistences while comparing the RNA-seq abundanceamong independent samples.Despite the theoretical and empirical demonstration that theunits of mRNA abundance in terms of RPKM or FPKM differ be-tween samples (Wagner et al., 2012), the most popular computa-tional tools used by the research community still quantify theRNA-Seq abundance in terms of RPKM or FPKM. Although, thereare emergent computational tools integrating TPM calculations intheir pipelines, such as Salmon (Patro et al., 2017), the application islimited only to transcripts and cannot be used to estimate abundanceof any other genomic features. Researchers, who would like to usePublished by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US. 1960Bioinformatics, 35(11), 2019, 1960–1962doi: 10.1093/bioinformatics/bty896Advance Access Publication Date: 31 October 2018Applications NoteDownloaded from https://academic.oup.com/bioinformatics/article-abstract/35/11/1960/5150437 by Hungary EISZ Consortium user on 15 September 2019TPM for other quantifications, need to implement their own scriptsto calculate TPM values from raw read counts. This process requiresthe use of third-party software to calculate the raw read counts byintroducing an additional step in the workflow pipeline. This isprone to errors due to inconsistences on read assignment models andthe changing definition of genomic features in annotated databasessuch as GenBank and RefSeq (Coordinators, 2018).Considering the value of the RNA-Seq abundance quantificationand the lack of computational tools to process BAM files and calcu-late accurate TPM values directly from the alignments, we havedeveloped a software package named TPMCalculator.2 Materials and methodsTPMCalculator quantifies mRNA abundance directly from thealignments by parsing BAM files. The input parameters are the samegene transfer format (GTF) file used to generate the alignments, andone or multiple input BAM file(s) containing either single-end orpaired-end sequencing reads. The TPMCalculator output is com-prised of five files per sample reporting the TPM values and rawread counts for genes, transcripts, exons and introns.The model to describe the genomic features used for a gene iscreated from the GTF provided by the user. TPMCalculator per-forms two transformations which are executed on the genomic coor-dinates generating regions for the genes that include the exons and‘pure’ intron regions as shown in Supplementary Figure S1. The firsttransformation creates overlapped exons for all alternative splicedforms of the gene. A single gene model is generated with uniqueexons and introns which includes the sequence of all exonic regions.The second transformation creates a list of pure intron regions thatreplace those generated by the first transformation. We should em-phasize that only the intron regions included are from regions thatare not from overlapping exons of other genes. Reporting TPM val-ues for these unique introns permits further identification of alterna-tive splicing events such as intron retention. Additionally, a set ofnon-overlapped gene features (exons and introns) are generated andused for TPM calculation.3 ResultsTPMCalculator is a one-step software package to quantify mRNAabundance for several genomic features including genes, transcripts,exons and introns. The program processes RNA-Seq alignments inBAM file format producing text files with TPM values, raw readcounts and feature lengths for each genomic feature.To validate our software, we calculate the Pearson correlationcoefficient between TPM and FPKM for normalized expression val-ues using RNA-Seq data of 1256 samples from the TCGA-BRCAproject (Koboldt et al., 2012). The FPKM values were calculatedusing the RSeQC package (Wang et al., 2012) as described in theSupplementary Material.Figure 1 shows the correlation coefficients obtained in this com-parison where 98.6% (1238 samples) correlated above 0.8 for theMAPQ¼0. Though, for the rest of MAPQ values, all samples werecorrelated with a correlation coefficient above 0.8 with the excep-tion of the MAPQ¼255 where one sample correlated with 0.69.Additionally, the correlation coefficients were calculated for theraw reads counts reported by TPMCalculator, HTSeq (Anders et al.,2015) and featureCounts (Liao et al., 2014). The correlation coeffi-cient between raw read counts between TPMCalculator and HTSeqwas above 0.9 for 99.2% (1246) samples. Only 10 samples showedno correlation with a coefficient below 0.2. TPMCalculator andfeatureCounts correlation coefficients were above 0.99 for 99.9% ofthe samples. Only one sample showed no correlation with correl-ation coefficient¼0.1. Samples with low correlation are shown inSupplementary Table S3.TPMCalculator reports, in one single analysis, raw read countsand TPM values for genes, transcripts, exons and introns. Currentlyavailable tools are unable to generate a complete set of data for allgenomic features. Additionally, TPMCalculator reduces the com-pute time and the resource requirements of RNA-Seq pipelines byeliminating several steps. TPMCalculator processes BAM files ofsize 7.0 GB in 20 min requiring only 4 GB of RAM. This is an opensource project available on the NCBI GitHub repository at https://github.com/ncbi/TPMCalculator.AcknowledgementWe would like to thank Iris Zhu for comments on the manuscript.FundingThis work was supported by the Intramural Research Program of theNational Institutes of Health, National Library of Medicine and NationalCenter for Biotechnology Information (NIH, NLM, NCBI). L.S.P. was sup-ported by an appointment to the National Center for BiotechnologyInformation Scientific Visitors Program at Oak Ridge Institute for Scienceand Education (ORISE) through an interagency agreement between the U.S.Department of Energy and the National Library of Medicine and by theUNKP-17-3 New National Excellence Program of Ministry of HumanCapacities (Hungary).Conflict of Interest: none declared.ReferencesAnders,S. et al. (2015) HTSeq–a Python framework to work withhigh-throughput sequencing data. Bioinformatics, 31, 166–169.Coordinators,N.R. (2018) Database resources of the National Center forBiotechnology Information. Nucleic Acids Res., 46, D8–D13.Koboldt,D.C. et al. (2012) Comprehensive molecular portraits of humanbreast tumours. Nature, 490, 61–70.Liao,Y. et al. (2014) featureCounts: an efficient general purpose program forassigning sequence reads to genomic features. Bioinformatics, 30, 923–930.Mortazavi,A. et al. (2008) Mapping and quantifying mammalian transcrip-tomes by RNA-Seq. Nat. Methods, 5, 621–628.Fig. 1. Correlation coefficient calculated for 1256 samples betweenTPMCalculator (ExonTPM) and RSeQC FPKMTPMCalculator 1961Downloaded from https://academic.oup.com/bioinformatics/article-abstract/35/11/1960/5150437 by Hungary EISZ Consortium user on 15 September 2019Patro,R. et al. (2017) Salmon provides fast and bias-aware quantification oftranscript expression. Nat. Methods, 14, 417–419.Trapnell,C. et al. (2010) Transcript assembly and quantification by RNA-Seqreveals unannotated transcripts and isoform switching during cell differenti-ation. Nat. Biotechnol., 28, 511–515.Wagner,G.P. et al. (2012) Measurement of mRNA abundance using RNA-seqdata: RPKM measure is inconsistent among samples. Theory Biosci., 131,281–285.Wang,L. et al. (2012) RSeQC: quality control of RNA-seq experiments.Bioinformatics, 28, 2184–2185.1962 R.V.Alvarez et al.Downloaded from https://academic.oup.com/bioinformatics/article-abstract/35/11/1960/5150437 by Hungary EISZ Consortium user on 15 September 2019

TPMCalculator: one-step software to quantify mRNA abundance of genomic features.

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TPMCalculator: one-step software to quantify mRNA abundance of genomic features.

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