Rob Patro

Salmon provides fast and bias-aware quantification of transcript expression

We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA–seq reads. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. It is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which, as we demonstrate here, substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.

TransRate: reference-free quality assessment of de novo transcriptome assemblies

TransRate is a tool for reference-free quality assessment of de novo transcriptome assemblies. Using only the sequenced reads and the assembly as input, we show that multiple common artifacts of de novo transcriptome assembly can be readily detected. These include chimeras, structural errors, incomplete assembly, and base errors. TransRate evaluates these errors to produce a diagnostic quality score for each contig, and these contig scores are integrated to evaluate whole assemblies. Thus, TransRate can be used for de novo assembly filtering and optimization as well as comparison of assemblies generated using different methods from the same input reads. Applying the method to a data set of 155 published de novo transcriptome assemblies, we deconstruct the contribution that assembly method, read length, read quantity, and read quality make to the accuracy of de novo transcriptome assemblies and reveal that variance in the quality of the input data explains 43% of the variance in the quality of published de novo transcriptome assemblies. Because TransRate is reference-free, it is suitable for assessment of assemblies of all types of RNA, including assemblies of long noncoding RNA, rRNA, mRNA, and mixed RNA samples.

Salmon: Accurate, Versatile and Ultrafast Quantification from RNA-seq Data using Lightweight-Alignment

Transcript quantication is a central task in the analysis of RNA-seq data. Accurate computational methods for the quantication of transcript abundances are essential for downstream analysis. However, most existing approaches are much slower than is necessary for their degree of accuracy. We introduce Salmon, a novel method and software tool for transcript quantication that exhibits state-of-the-art accuracy while being signicantly faster than most other tools. Salmon achieves this through the combined application of a two-phase inference procedure, a reduced data representation, and a novel lightweight read alignment algorithm. Salmon is written in C++11, and is available under the GPL v3 license as open-source software at https://combine-lab.github.io/salmon.We introduce Salmon, a new method for quantifying transcript abundance from RNA-seq reads that is highly-accurate and very fast. Salmon is the first transcriptome-wide quantifier to model and correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis compared to existing methods that do not account for these biases. Salmon achieves its speed and accuracy by combining a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. These innovations yield both exceptional accuracy and order-of-magnitude speed benefits over alignment-based methods.

Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference

Transcript quantication is a central task in the analysis of RNA-seq data. Accurate computational methods for the quantication of transcript abundances are essential for downstream analysis. However, most existing approaches are much slower than is necessary for their degree of accuracy. We introduce Salmon, a novel method and software tool for transcript quantication that exhibits state-of-the-art accuracy while being signicantly faster than most other tools. Salmon achieves this through the combined application of a two-phase inference procedure, a reduced data representation, and a novel lightweight read alignment algorithm. Salmon is written in C++11, and is available under the GPL v3 license as open-source software at https://combine-lab.github.io/salmon.We introduce Salmon, a new method for quantifying transcript abundance from RNA-seq reads that is highly-accurate and very fast. Salmon is the first transcriptome-wide quantifier to model and correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis compared to existing methods that do not account for these biases. Salmon achieves its speed and accuracy by combining a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. These innovations yield both exceptional accuracy and order-of-magnitude speed benefits over alignment-based methods.

AtRTD – a comprehensive reference transcript dataset resource for accurate quantification of transcript‐specific expression in Arabidopsis thaliana

Summary RNA‐sequencing (RNA‐seq) allows global gene expression analysis at the individual transcript level. Accurate quantification of transcript variants generated by alternative splicing (AS) remains a challenge. We have developed a comprehensive, nonredundant Arabidopsis reference transcript dataset (AtRTD) containing over 74 000 transcripts for use with algorithms to quantify AS transcript isoforms in RNA‐seq. The AtRTD was formed by merging transcripts from TAIR10 and novel transcripts identified in an AS discovery project. We have estimated transcript abundance in RNA‐seq data using the transcriptome‐based alignment‐free programmes Sailfish and Salmon and have validated quantification of splicing ratios from RNA‐seq by high resolution reverse transcription polymerase chain reaction (HR RT‐PCR). Good correlations between splicing ratios from RNA‐seq and HR RT‐PCR were obtained demonstrating the accuracy of abundances calculated for individual transcripts in RNA‐seq. The AtRTD is a resource that will have immediate utility in analysing Arabidopsis RNA‐seq data to quantify differential transcript abundance and expression.

Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing Data

The inference of biological relatedness from DNA sequence data has a wide array of applications, such as in the study of human disease, anthropology and ecology. One of the most common analytical frameworks for performing this inference is to genotype individuals for large numbers of independent genomewide markers and use population allele frequencies to infer the probability of identity-by-descent (IBD) given observed genotypes. Current implementations of this class of methods assume genotypes are known without error. However, with the advent of 2nd generation sequencing data there are now an increasing number of situations where the confidence attached to a particular genotype may be poor because of low coverage. Such scenarios may lead to biased estimates of the kinship coefficient, ε We describe an approach that utilizes genotype likelihoods rather than a single observed best genotype to estimate ϕ and demonstrate that we can accurately infer relatedness in both simulated and real 2nd generation sequencing data from a wide variety of human populations down to at least the third degree when coverage is as low as 2x for both individuals, while other commonly used methods such as PLINK exhibit large biases in such situations. In addition the method appears to be robust when the assumed population allele frequencies are diverged from the true frequencies for realistic levels of genetic drift. This approach has been implemented in the C++ software lcMLkin.

Optimizing Read Reversals for Sequence Compression

New generation sequencing technologies produce massive data sets of millions of reads, making the compression of sequence read files an important problem. The sequential order of the reads in these files typically conveys no biologically significant information, providing the freedom to reorder them so as to facilitate compression. Similarly, for many problems the orientation of the reads (original or reverse complement) are indistinguishable from an information-theoretic perspective, providing the freedom to optimize the orientation of each read.

Compression of short-read sequences using path encoding

Storing, transmitting, and archiving the amount of data produced by next generation sequencing is becoming a significant computational burden. For example, large-scale RNA-seq meta-analyses may now routinely process tens of terabytes of sequence. We present here an approach to biological sequence compression that reduces the difficulty associated with managing the data produced by large-scale transcriptome sequencing. Our approach offers a new direction by sitting between pure reference-based compression and reference-free compression and combines much of the benefit of reference-based approaches with the flexibility of de novo encoding. Our method, called path encoding, draws a connection between storing paths in de Bruijn graphs — a common task in genome assembly — and context-dependent arithmetic coding. Supporting this method is a system, called a bit tree, to compactly store sets of kmers that is of independent interest. Using these techniques, we are able to encode RNA-seq reads using 3% – 11% of the space of the sequence in raw FASTA files, which is on average more than 34% smaller than recent competing approaches. We also show that even if the reference is very poorly matched to the reads that are being encoded, good compression can still be achieved.

Alevin: An integrated method for dscRNA-seq quantification

We introduce alevin, an efficient pipeline for gene quantification from dscRNA-seq (droplet-based single-cell RNA-seq) data. Alevin is an end-to-end quantification pipeline that starts from sample-demultiplexed FASTQ files and generates gene-level counts for two popular droplet-based sequencing protocols (drop-seq [1], and 10x-chromium [2]). Importantly, alevin handles all processing internally, avoiding reliance on external pipeline programs, and the need to write large intermediate files to disk. Alevin adopts efficient algorithms for cellular-barcode whitelist generation, cellular-barcode correction, lightweight per-cell UMI deduplication and quantification. This integrated solution allows alevin to process data much faster (typically ∼ 10 times faster) than other approaches, while also working within a reasonable memory budget. This enables full, end-to-end analysis for single-cell human experiment consisting of ∼ 4500 cells with 335 Million reads with 13G of RAM and 8 threads (of an Intel Xeon E5-2699 v4 CPU) in 27 minutesWe introduce alevin, a fast end-to-end pipeline to process droplet-based single cell RNA sequencing data, which performs cell barcode detection, read mapping, unique molecular identifier deduplication, gene count estimation, and cell barcode whitelisting. Alevin9s approach to UMI deduplication accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads, and improves the accuracy of gene abundance estimates. Alevin is written in C++14, and is integrated into the salmon tool available at https://github.com/COMBINE-lab/salmon.

A junction coverage compatibility score to quantify the reliability of transcript abundance estimates and annotation catalogs

Most methods for statistical analysis of RNA-seq data take a matrix of abundance estimates for some type of genomic features as their input, and consequently the quality of any obtained results are directly dependent on the quality of these abundances. Here, we present the junction coverage compatibility (JCC) score, which provides a way to evaluate the reliability of transcript-level abundance estimates as well as the accuracy of transcript annotation catalogs. It works by comparing the observed number of reads spanning each annotated splice junction in a genomic region to the predicted number of junction-spanning reads, inferred from the estimated transcript abundances and the genomic coordinates of the corresponding annotated transcripts. We show that while most genes show good agreement between the observed and predicted junction coverages, there is a small set of genes that do not. Genes with poor agreement are found regardless of the method used to estimate transcript abundances, and the corresponding transcript abundances should be treated with care in any downstream analyses.