Quantification Pipeline Research Articles

Homoeologous gene expression and co-expression network analyses and evolutionary inference in allopolyploids.

Polyploidy is a widespread phenomenon throughout eukaryotes. Due to the coexistence of duplicated genomes, polyploids offer unique challenges for estimating gene expression levels, which is essential for understanding the massive and various forms of transcriptomic responses accompanying polyploidy. Although previous studies have explored the bioinformatics of polyploid transcriptomic profiling, the causes and consequences of inaccurate quantification of transcripts from duplicated gene copies have not been addressed. Using transcriptomic data from the cotton genus (Gossypium) as an example, we present an analytical workflow to evaluate a variety of bioinformatic method choices at different stages of RNA-seq analysis, from homoeolog expression quantification to downstream analysis used to infer key phenomena of polyploid expression evolution. In general, EAGLE-RC and GSNAP-PolyCat outperform other quantification pipelines tested, and their derived expression dataset best represents the expected homoeolog expression and co-expression divergence. The performance of co-expression network analysis was less affected by homoeolog quantification than by network construction methods, where weighted networks outperformed binary networks. By examining the extent and consequences of homoeolog read ambiguity, we illuminate the potential artifacts that may affect our understanding of duplicate gene expression, including an overestimation of homoeolog co-regulation and the incorrect inference of subgenome asymmetry in network topology. Taken together, our work points to a set of reasonable practices that we hope are broadly applicable to the evolutionary exploration of polyploids.

Atlas-based GABA mapping with 3D MEGA-MRSI: Cross-correlation to single-voxel MRS.

The purpose of this work is to develop and validate a new atlas-based metabolite quantification pipeline for edited magnetic resonance spectroscopic imaging (MEGA-MRSI) that enables group comparisons of brain structure-specific GABA levels. By using brain structure masks segmented from high-resolution MPRAGE images and coregistering these to MEGA-LASER 3D MRSI data, an automated regional quantification of neurochemical levels is demonstrated for the example of the thalamus. Thalamic gamma-aminobutyric acid + coedited macromolecules (GABA+) levels from 21 healthy subjects scanned at 3 T were cross-validated both against a single-voxel MEGA-PRESS acquisition in the same subjects and same scan sessions, as well as alternative MRSI processing techniques (ROI approach, four-voxel approach) using Pearson correlation analysis. In addition, reproducibility was compared across the MRSI processing techniques in test-retest data from 14 subjects. The atlas-based approach showed a significant correlation with SV MEGA-PRESS (correlation coefficient r [GABA+] = 0.63, P < 0.0001). However, the actual values for GABA+, NAA, tCr, GABA+/tCr and tNAA/tCr obtained from the atlas-based approach showed an offset to SV MEGA-PRESS levels, likely due to the fact that on average the thalamus mask used for the atlas-based approach only occupied 30% of the SVS volume, ie, somewhat different anatomies were sampled. Furthermore, the new atlas-based approach showed highly reproducible GABA+/tCr values with a low median coefficient of variance of 6.3%. In conclusion, the atlas-based metabolite quantification approach enables a more brain structure-specific comparison of GABA+ and other neurochemical levels across populations, even when using an MRSI technique with only cm-level resolution. This approach was successfully cross-validated against the typically used SVS technique as well as other different MRSI analysis methods, indicating the robustness of this quantification approach.

Quantifying the Metabolic Signature of Multiple Sclerosis by in vivo Proton Magnetic Resonance Spectroscopy: Current Challenges and Future Outlook in the Translation From Proton Signal to Diagnostic Biomarker.

Proton magnetic resonance spectroscopy (1H-MRS) offers a growing variety of methods for querying potential diagnostic biomarkers of multiple sclerosis in living central nervous system tissue. For the past three decades, 1H-MRS has enabled the acquisition of a rich dataset suggestive of numerous metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord of individuals with multiple sclerosis, but this body of information is not free of seeming internal contradiction. The use of 1H-MRS signals as diagnostic biomarkers depends on reproducible and generalizable sensitivity and specificity to disease state that can be confounded by a multitude of influences, including experiment group classification and demographics; acquisition sequence; spectral quality and quantifiability; the contribution of macromolecules and lipids to the spectroscopic baseline; spectral quantification pipeline; voxel tissue and lesion composition; T1 and T2 relaxation; B1 field characteristics; and other features of study design, spectral acquisition and processing, and metabolite quantification about which the experimenter may possess imperfect or incomplete information. The direct comparison of 1H-MRS data from individuals with and without multiple sclerosis poses a special challenge in this regard, as several lines of evidence suggest that experimental cohorts may differ significantly in some of these parameters. We review the existing findings of in vivo 1H-MRS on central nervous system metabolic abnormalities in multiple sclerosis and its subtypes within the context of study design, spectral acquisition and processing, and metabolite quantification and offer an outlook on technical considerations, including the growing use of machine learning, by future investigations into diagnostic biomarkers of multiple sclerosis measurable by 1H-MRS.

Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level.

SummaryWith the advancements of high-throughput single-cell RNA-sequencing protocols, there has been a rapid increase in the tools available to perform an array of analyses on the gene expression data that results from such studies. For example, there exist methods for pseudo-time series analysis, differential cell usage, cell-type detection RNA-velocity in single cells, etc. Most analysis pipelines validate their results using known marker genes (which are not widely available for all types of analysis) and by using simulated data from gene-count-level simulators. Typically, the impact of using different read-alignment or unique molecular identifier (UMI) deduplication methods has not been widely explored. Assessments based on simulation tend to start at the level of assuming a simulated count matrix, ignoring the effect that different approaches for resolving UMI counts from the raw read data may produce. Here, we present minnow, a comprehensive sequence-level droplet-based single-cell RNA-sequencing (dscRNA-seq) experiment simulation framework. Minnow accounts for important sequence-level characteristics of experimental scRNA-seq datasets and models effects such as polymerase chain reaction amplification, cellular barcodes (CB) and UMI selection and sequence fragmentation and sequencing. It also closely matches the gene-level ambiguity characteristics that are observed in real scRNA-seq experiments. Using minnow, we explore the performance of some common processing pipelines to produce gene-by-cell count matrices from droplet-bases scRNA-seq data, demonstrate the effect that realistic levels of gene-level sequence ambiguity can have on accurate quantification and show a typical use-case of minnow in assessing the output generated by different quantification pipelines on the simulated experiment.Supplementary information Supplementary data are available at Bioinformatics online.

Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation.

Population imaging studies generate data for developing and implementing personalised health strategies to prevent, or more effectively treat disease. Large prospective epidemiological studies acquire imaging for pre-symptomatic populations. These studies enable the early discovery of alterations due to impending disease, and enable early identification of individuals at risk. Such studies pose new challenges requiring automatic image analysis. To date, few large-scale population-level cardiac imaging studies have been conducted. One such study stands out for its sheer size, careful implementation, and availability of top quality expert annotation; the UK Biobank (UKB). The resulting massive imaging datasets (targeting ca. 100,000 subjects) has put published approaches for cardiac image quantification to the test. In this paper, we present and evaluate a cardiac magnetic resonance (CMR) image analysis pipeline that properly scales up and can provide a fully automatic analysis of the UKB CMR study. Without manual user interactions, our pipeline performs end-to-end image analytics from multi-view cine CMR images all the way to anatomical and functional bi-ventricular quantification. All this, while maintaining relevant quality controls of the CMR input images, and resulting image segmentations. To the best of our knowledge, this is the first published attempt to fully automate the extraction of global and regional reference ranges of all key functional cardiovascular indexes, from both left and right cardiac ventricles, for a population of 20,000 subjects imaged at 50 time frames per subject, for a total of one million CMR volumes. In addition, our pipeline provides 3D anatomical bi-ventricular models of the heart. These models enable the extraction of detailed information of the morphodynamics of the two ventricles for subsequent association to genetic, omics, lifestyle habits, exposure information, and other information provided in population imaging studies. We validated our proposed CMR analytics pipeline against manual expert readings on a reference cohort of 4620 subjects with contour delineations and corresponding clinical indexes. Our results show broad significant agreement between the manually obtained reference indexes, and those automatically computed via our framework. 80.67% of subjects were processed with mean contour distance of less than 1pixel, and 17.50% with mean contour distance between 1 and 2pixels. Finally, we compare our pipeline with a recently published approach reporting on UKB data, and based on deep learning. Our comparison shows similar performance in terms of segmentation accuracy with respect to human experts.

Multi-tissue observation of the long non-coding RNA effects on sexually biased gene expression in cattle.

ObjectiveRecent studies have implied that gene expression has high tissue-specificity, and therefore it is essential to investigate gene expression in a variety of tissues when performing the transcriptomic analysis. In addition, the gradual increase of long non-coding RNA (lncRNA) annotation database has increased the importance and proportion of mapped reads accordingly.MethodsWe employed simple statistical models to detect the sexually biased/dimorphic genes and their conjugate lncRNAs in 40 RNA-seq samples across two factors: sex and tissue. We employed two quantification pipeline: mRNA annotation only and mRNA+lncRNA annotation.ResultsAs a result, the tissue-specific sexually dimorphic genes are affected by the addition of lncRNA annotation at a non-negligible level. In addition, many lncRNAs are expressed in a more tissue-specific fashion and with greater variation between tissues compared to protein-coding genes. Due to the genic region lncRNAs, the differentially expressed gene list changes, which results in certain sexually biased genes to become ambiguous across the tissues.ConclusionIn a past study, it has been reported that tissue-specific patterns can be seen throughout the differentially expressed genes between sexes in cattle. Using the same dataset, this study used a more recent reference, and the addition of conjugate lncRNA information, which revealed alterations of differentially expressed gene lists that result in an apparent distinction in the downstream analysis and interpretation. We firmly believe such misquantification of genic lncRNAs can be vital in both future and past studies.

A Comparison of Two Hyperpolarized 129Xe MRI Ventilation Quantification Pipelines: The Effect of Signal to Noise Ratio

Hyperpolarized 129Xe MRI enables quantitative evaluation of regional ventilation. To this end, multiple classifiers have been proposed to determine ventilation defect percentage (VDP) as well as other cluster populations. However, consensus has not yet been reached regarding which of these methods to deploy for multicenter clinical trials. Here, we compare two published classification techniques-linear-binning and adaptive K-means-to establish their limits of agreement and their robustness against reduced signal-to-noise ratio (SNR). A total of 29 subjects (age: 38.4 ± 19.0 years) were retrospectively identified for inter-method comparison. For each 129Xe ventilation image, 7 images with reduced SNR were generated with equal decrements relative to the native SNR. All 8 sets of images were then analyzed using both methods independently to classify all lung voxels into four clusters: VDP, low-, medium-, and high-ventilation-percentage (LVP, MVP and HVP). For each cluster, the percentage of the lung it comprised was compared between the two methods, as well as how these values persisted as SNR was degraded. The limits of agreement for calculating VDP were [+0.2%, +4.0%] with a +1.5% bias for binning relative to K-means. However, the inter-method agreement for the other clusters was moderate, with biases of -5.7%, 8.1%, and -4.0% for LVP, MVP, and HVP, respectively. As SNR decreased below ∼4, both methods began reporting values that deviated substantially from the native image. By requiring VDP to remain within ≤1.8% of that calculated from the native image, the minimum tolerable SNR values were 2.4 ± 1.0 for the linear-binning, and 3.5 ± 1.5 for the K-means. Both methods agree well in quantifying VDP, but agreement for LVP and MVP remains variable. We suggest a required SNR threshold be two standard deviations above the minimum value of 3.5 ± 1.5 for robust determination of VDP, suggesting a minimum SNR of 6.6. However, robust quantification of the ventilated clusters required an SNR of 13.4.

Integration of an automated identification-quantification pipeline and statistical techniques for pyrolysis GC/MS tracking of the molecular fingerprints of natural organic matter

Pyrolysis gas chromatography/mass spectrometry is a potent tool for studying the molecular fingerprints of natural organic matter (NOM). With advances in analytical techniques, a pyrogram generally consists over a hundred pyrolysates, which increases the difficulty of interpreting the associated data. Here, we propose a systematic approach that includes an automatic peak identification and quantification pipeline and statistical techniques for the analysis of NOM. White oak (Quercus alba) and forest floor litter samples from a 48-week field decomposition study including sites along a soil moisture gradient were used to evaluate the applicability. An analysis of variance of the chemical classes indicated that the composition differed among sites, although a trend following the moisture gradient was not observed. Factor analysis of the pyrolysates clearly identified two decomposition stages in both types of decomposition. For the oak litter, 2,6-dimethoxy-phenol originating from syringyl lignin was dominant in the early stage, whereas for the forest floor litter, 2-methoxy-4-vinylphenol was enriched in the early stage, while 4-ethyl-2-methoxyphenol and 3-allyl-6-methoxyphenol were dominant in the later stage. These compounds originated from guaiacyl lignin, which suggested that guaiacyl lignin was relatively constant. The proposed approach provides a convenient and effective way to study the chemical composition of NOM.

Past-in-the-Future. Peak detection improves targeted mass spectrometry imaging

Mass spectrometry imaging is a valuable tool for visualizing the localization of drugs in tissues, a critical issue especially in cancer pharmacology where treatment failure may depend on poor drug distribution within the tumours. Proper preprocessing procedures are mandatory to obtain quantitative data of drug distribution in tumours, even at low intensity, through reliable ion peak identification and integration. We propose a simple preprocessing and quantification pipeline. This pipeline was designed starting from classical peak integration methods, developed when “microcomputers” became available for chromatography, now applied to MSI. This pre-processing approach is based on a novel method using the fixed mass difference between the analyte and its 5 d derivatives to set up a mass range gate. We demonstrate the use of this pipeline for the evaluating the distribution of the anticancer drug paclitaxel in tumour sections. The procedure takes advantage of a simple peak analysis and allows to quantify the drug concentration in each pixel with a limit of detection below 0.1 pmol mm-2 or 10 μg g−1. Quantitative images of paclitaxel distribution in different tumour models were obtained and average paclitaxel concentrations were compared with HPLC measures in the same specimens, showing <20% difference. The scripts are developed in Python and available through GitHub, at github.com/FrancescaFalcetta/Imaging_of_drugs_distribution_and_quantifications.git.

A Galerkin isogeometric method for Karhunen–Loève approximation of random fields

This paper marks the debut of a Galerkin isogeometric method for solving a Fredholm integral eigenvalue problem, enabling random field discretization by means of the Karhunen–Loève expansion. The method involves a Galerkin projection onto a finite-dimensional subspace of a Hilbert space, basis splines (B-splines) and non-uniform rational B-splines (NURBS) spanning the subspace, and standard methods of eigensolutions. Compared with the existing Galerkin methods, such as the finite-element and mesh-free methods, the NURBS-based isogeometric method upholds exact geometrical representation of the physical or computational domain and exploits regularity of basis functions delivering globally smooth eigensolutions. Therefore, the introduction of the isogeometric method for random field discretization is not only new; it also offers a few computational advantages over existing methods. In the big picture, the use of NURBS for random field discretization enriches the isogeometric paradigm. As a result, an uncertainty quantification pipeline of the future can be envisioned where geometric modeling, stress analysis, and stochastic simulation are all integrated using the same building blocks of NURBS. Three numerical examples, including a three-dimensional random field discretization problem, illustrate the accuracy and convergence properties of the isogeometric method for obtaining eigensolutions.

HLA and proteasome expression body map

BackgroundThe presentation of HLA peptide complexes to T cells is a highly regulated and tissue specific process involving multiple transcriptionally controlled cellular components. The extensive polymorphism of HLA genes and the complex composition of the proteasome make it difficult to map their expression profiles across tissues.MethodsHere we applied a tailored gene quantification pipeline to 4323 publicly available RNA-Seq datasets representing 55 normal tissues and cell types to examine expression profiles of (classical and non-classical) HLA class I, class II and proteasomal genes.ResultsWe generated the first comprehensive expression atlas of antigen presenting-related genes across 56 normal tissues and cell types, including immune cells, pancreatic islets, platelets and hematopoietic stem cells. We found a surprisingly heterogeneous HLA expression pattern with up to 100-fold difference in intra-tissue median HLA abundances. Cells of the immune system and lymphatic organs expressed the highest levels of classical HLA class I (HLA-A,-B,-C), class II (HLA-DQA1,-DQB1,-DPA1,-DPB1,-DRA,-DRB1) and non-classical HLA class I (HLA-E,-F) molecules, whereas retina, brain, muscle, megakaryocytes and erythroblasts showed the lowest abundance. In contrast, we identified a distinct and highly tissue-restricted expression pattern of the non-classical class I gene HLA-G in placenta, pancreatic islets, pituitary gland and testis. While the constitutive proteasome showed relatively constant expression across all tissues, we found the immunoproteasome to be enriched in lymphatic organs and almost absent in immune privileged tissues.ConclusionsHere, we not only provide a reference catalog of tissue and cell type specific HLA expression, but also highlight extremely variable expression of the basic components of antigen processing and presentation in different cell types. Our findings indicate that low expression of classical HLA class I molecules together with lack of immunoproteasome components as well as upregulation of HLA-G may be of key relevance to maintain tolerance in immune privileged tissues.

Developmental transcriptomics of the brittle star Amphiura filiformis reveals gene regulatory network rewiring in echinoderm larval skeleton evolution

BackgroundAmongst the echinoderms the class Ophiuroidea is of particular interest for its phylogenetic position, ecological importance and developmental and regenerative biology. However, compared to other echinoderms, notably echinoids (sea urchins), relatively little is known about developmental changes in gene expression in ophiuroids. To address this issue, we have generated and assembled a large RNAseq data set of four key stages of development in the brittle star Amphiura filiformis and a de novo reference transcriptome of comparable quality to that of a model echinoderm—the sea urchin Strongylocentrotus purpuratus. Furthermore, we provide access to the new data via a web interface: http://www.echinonet.eu/shiny/Amphiura_filiformis/.ResultsWe have identified highly conserved genes associated with the development of a biomineralised skeleton. We also identify important class-specific characters, including the independent duplication of the msp130 class of genes in different echinoderm classes and the unique occurrence of spicule matrix (sm) genes in echinoids. Using a new quantification pipeline for our de novo transcriptome, validated with other methodologies, we find major differences between brittle stars and sea urchins in the temporal expression of many transcription factor genes. This divergence in developmental regulatory states is more evident in early stages of development when cell specification begins, rather than when cells initiate differentiation.ConclusionsOur findings indicate that there has been a high degree of gene regulatory network rewiring and clade-specific gene duplication, supporting the hypothesis of a convergent evolution of larval skeleton development in echinoderms.

Analysis of primary microRNA loci from nascent transcriptomes reveals regulatory domains governed by chromatin architecture.

Changes in mature microRNA (miRNA) levels that occur downstream of signaling cascades play an important role during human development and disease. However, the regulation of primary microRNA (pri-miRNA) genes remains to be dissected in detail. To address this, we followed a data-driven approach and developed a transcript identification, validation and quantification pipeline for characterizing the regulatory domains of pri-miRNAs. Integration of 92 nascent transcriptomes and multilevel data from cells arising from ecto-, endo- and mesoderm lineages reveals cell type-specific expression patterns, allows fine-resolution mapping of transcription start sites (TSS) and identification of candidate regulatory regions. We show that inter- and intragenic pri-miRNA transcripts span vast genomic regions and active TSS locations differ across cell types, exemplified by the mir-29a∼29b-1, mir-100∼let-7a-2∼125b-1 and miR-221∼222 clusters. Considering the presence of multiple TSS as an important regulatory feature at miRNA loci, we developed a strategy to quantify differential TSS usage. We demonstrate that the TSS activities associate with cell type-specific super-enhancers, differential stimulus responsiveness and higher-order chromatin structure. These results pave the way for building detailed regulatory maps of miRNA loci.

Analysis of primary microRNA loci from nascent transcriptomes reveals regulatory domains governed by chromatin architecture.

Changes in mature microRNA (miRNA) levels that occur downstream of signaling cascades play an important role during human development and disease. However, the regulation of primary microRNA (pri-miRNA) genes remains to be dissected in detail. To address this, we followed a data-driven approach and developed a transcript identification, validation and quantification pipeline for characterizing the regulatory domains of pri-miRNAs. Integration of 92 nascent transcriptomes and multilevel data from cells arising from ecto-, endo- and mesoderm lineages reveals cell type-specific expression patterns, allows fine-resolution mapping of transcription start sites (TSS) and identification of candidate regulatory regions. We show that inter- and intragenic pri-miRNA transcripts span vast genomic regions and active TSS locations differ across cell types, exemplified by the mir-29a∼29b-1, mir-100∼let-7a-2∼125b-1 and miR-221∼222 clusters. Considering the presence of multiple TSS as an important regulatory feature at miRNA loci, we developed a strategy to quantify differential TSS usage. We demonstrate that the TSS activities associate with cell type-specific super-enhancers, differential stimulus responsiveness and higher-order chromatin structure. These results pave the way for building detailed regulatory maps of miRNA loci.

Arkas: Rapid reproducible RNAseq analysis

The recently introduced Kallisto pseudoaligner has radically simplified the quantification of transcripts in RNA-sequencing experiments. We offer cloud-scale RNAseq pipelines Arkas-Quantification, which deploys Kallisto for parallel cloud computations, and Arkas-Analysis, which annotates the Kallisto results by extracting structured information directly from source FASTA files with per-contig metadata and calculates the differential expression and gene-set enrichment analysis on both coding genes and transcripts. The biologically informative downstream gene-set analysis maintains special focus on Reactome annotations while supporting ENSEMBL transcriptomes. The Arkas cloud quantification pipeline includes support for custom user-uploaded FASTA files, selection for bias correction and pseudoBAM output. The option to retain pseudoBAM output for structural variant detection and annotation provides a middle ground between de novo transcriptome assembly and routine quantification, while consuming a fraction of the resources used by popular fusion detection pipelines. Illumina's BaseSpace cloud computing environment, where these two applications are hosted, offers a massively parallel distributive quantification step for users where investigators are better served by cloud-based computing platforms due to inherent efficiencies of scale.

MiRmine: a database of human miRNA expression profiles

MicroRNAs (miRNAs) are small non-coding RNAs that are involved in post-transcriptional regulation of gene expression. In this high-throughput sequencing era, a tremendous amount of RNA-seq data is accumulating, and full utilization of publicly available miRNA data is an important challenge. These data are useful to determine expression values for each miRNA, but quantification pipelines are in a primitive stage and still evolving; there are many factors that affect expression values significantly. We used 304 high-quality microRNA sequencing (miRNA-seq) datasets from NCBI-SRA and calculated expression profiles for different tissues and cell-lines. In each miRNA-seq dataset, we found an average of more than 500 miRNAs with higher than 5x coverage, and we explored the top five highly expressed miRNAs in each tissue and cell-line. This user-friendly miRmine database has options to retrieve expression profiles of single or multiple miRNAs for a specific tissue or cell-line, either normal or with disease information. Results can be displayed in multiple interactive, graphical and downloadable formats. http://guanlab.ccmb.med.umich.edu/mirmine. bharatpa@umich.edu. Supplementary data are available at Bioinformatics online.

Falco: a quick and flexible single-cell RNA-seq processing framework on the cloud.

Single-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designed to efficiently process scRNA-seq data due to their limited scalability. Here we introduce Falco, a cloud-based framework to enable paralellization of existing RNA-seq processing pipelines using big data technologies of Apache Hadoop and Apache Spark for performing massively parallel analysis of large scale transcriptomic data. Using two public scRNA-seq datasets and two popular RNA-seq alignment/feature quantification pipelines, we show that the same processing pipeline runs 2.6-145.4 times faster using Falco than running on a highly optimized standalone computer. Falco also allows users to utilize low-cost spot instances of Amazon Web Services, providing a ∼65% reduction in cost of analysis. Falco is available via a GNU General Public License at https://github.com/VCCRI/Falco/. j.ho@victorchang.edu.au. Supplementary data are available at Bioinformatics online.

Erratum to: A benchmark for RNA-seq quantification pipelines.

Erratum to: A benchmark for RNA-seq quantification pipelines.

The exon quantification pipeline (EQP): a comprehensive approach to the quantification of gene, exon and junction expression from RNA-seq data.

The quantification of transcriptomic features is the basis of the analysis of RNA-seq data. We present an integrated alignment workflow and a simple counting-based approach to derive estimates for gene, exon and exon–exon junction expression. In contrast to previous counting-based approaches, EQP takes into account only reads whose alignment pattern agrees with the splicing pattern of the features of interest. This leads to improved gene expression estimates as well as to the generation of exon counts that allow disambiguating reads between overlapping exons. Unlike other methods that quantify skipped introns, EQP offers a novel way to compute junction counts based on the agreement of the read alignments with the exons on both sides of the junction, thus providing a uniformly derived set of counts. We evaluated the performance of EQP on both simulated and real Illumina RNA-seq data and compared it with other quantification tools. Our results suggest that EQP provides superior gene expression estimates and we illustrate the advantages of EQP's exon and junction counts. The provision of uniformly derived high-quality counts makes EQP an ideal quantification tool for differential expression and differential splicing studies. EQP is freely available for download at https://github.com/Novartis/EQP-cluster.

Erratum to: A benchmark for RNA-seq quantification pipelines.

After the publication of this work [1] it was noticed that there were typographical errors in the following equations: equation 5 in column 2, equation 7 in column 2, equation 8 in column 1.