Biology subjects

Pachter, L.

Publications and source records attributed to Pachter, L..

11 recordsLinked to original sources

bioRxiv2018

A direct comparison of genome alignment and transcriptome pseudoalignment

MotivationGenome alignment of reads is the first step of most genome analysis workflows. In the case of RNA-Seq, transcriptome pseudoalignment of reads is a fast alternative to genome alignment, but the different \"coordinate systems\" of the genome and transcriptome have made it difficult to perform direct comparisons between the approaches.\n\nResultsWe have developed tools for converting genome alignments to transcriptome pseudoalignments, and conversely, for projecting transcriptome pseudoalignments to genome alignments. Using these tools, we performed a direct comparison of genome alignment with transcriptome pseudoalignment. We find that both approaches produce similar quantifications. This means that for many applications genome alignment and transcriptome pseudoalignment are interchangeable.\n\nAvailability and Implementationbam2tcc is a C++14 software for converting alignments in SAM/BAM format to transcript compatibility counts (TCCs) and is available at https://github.com/pachterlab/bam2tcc. kallisto genomebam is a user option of kallisto that outputs a sorted BAM file in genome coordinates as part of transcriptome pseudoalignment. The feature has been released with kallisto v0.44.0, and is available at https://pachterlab.github.io/kallisto/.\n\nSupplementary MaterialN/A\n\nContactLior Pachter (lpachter@caltech.edu)

bioinformatics↗

bioRxiv2018

Expression reflects population structure

Population structure in genotype data has been extensively studied, and is revealed by looking at the principal components of the genotype matrix. However, no similar analysis of population structure in gene expression data has been conducted, in part because a naive principal components analysis of the gene expression matrix does not cluster by population. We identify a linear projection that reveals population structure in gene expression data. Our approach relies on the coupling of the principal components of genotype to the principal components of gene expression via canonical correlation analysis. Futhermore, we analyze the variance of each gene within the projection matrix to determine which genes significantly influence the projection. We identify thousands of significant genes, and show that a number of the top genes have been implicated in diseases that disproportionately impact African Americans.\n\nAuthor SummaryHigh dimensional, multi-modal genomics datasets are becoming increasingly common, which warrants investigation into analysis techniques that can reveal structure in the data without over-fitting. Here, we show that the coupling of principal component analysis to canonical correlation analysis offers an efficient approach to exploratory analysis of this kind of data. We apply this method to the GEUVADIS dataset of genotype and gene expression values of European and Yoruban individuals, finding as-of-yet unstudied population structure in the gene expression values. Moreover, many of the top genes identified by our method have been previously implicated in diseases that disproportionately impact African Americans.

genomics↗

bioRxiv2018

Highly Multiplexed Single-Cell RNA-seq for Defining Cell Population and Transcriptional Spaces

We describe a universal sample multiplexing method for single-cell RNA-seq in which cells are chemically labeled with identifying DNA oligonucleotides. Analysis of a 96-plex perturbation experiment revealed changes in cell population structure and transcriptional states that cannot be discerned from bulk measurements, establishing a cost effective means to survey cell populations from large experiments and clinical samples with the depth and resolution of single-cell RNA-seq.

genomics↗

bioRxiv2018

Identification of transcriptional signatures for cell types from single-cell RNA-Seq

Single-cell RNA-Seq makes it possible to characterize the transcriptomes of cell types and identify their transcriptional signatures via differential analysis. We present a fast and accurate method for discriminating cell types that takes advantage of the large numbers of cells that are assayed. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3 single-cell RNA-Seq that can identify previously undetectable marker genes.

bioinformatics↗

bioRxiv2017

Accurate design of translational output by a neural network model of ribosome distribution

Synonymous codon choice can have dramatic effects on ribosome speed, RNA stability, and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs, exposing a need for models of coding sequences that capture the full range of empirically observed variation. We present a method, Ixnos, that models this variation in translation elongation using a feedforward neural network to predict the translation elongation rate at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and quantified the impact of large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of possible translation speeds predicted with our model. We found that levels of the fluorescent protein in yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo, and that control of translation elongation alone is sufficient to produce large, quantitative differences in protein output.

genomics↗

bioRxiv2017

Gene-level differential analysis at transcript-level resolution

Gene-level differential expression analysis based on RNA-Seq is more robust, powerful and biologically actionable than transcript-level differential analysis. However aggregation of transcript counts prior to analysis results can mask transcript-level dynamics. We demonstrate that aggregating the results of transcript-level analysis allow for gene-level analysis with transcript-level resolution. We also show that p-value aggregation methods, typically used for meta-analyses, greatly increase the sensitivity of gene-level differential analyses. Furthermore, such aggregation can be applied directly to transcript compatibility counts obtained during pseudoalignment, thereby allowing for rapid and accurate model-free differential testing. The methods are general, allowing for testing not only of genes but also of any groups of transcripts, and we showcase an example where we apply them to perturbation analysis of gene ontologies.

bioinformatics↗

bioRxiv2017

Fusion detection and quantification by pseudoalignment

RNA sequencing in cancer cells is a powerful technique to detect chromosomal rearrangements, allowing for de novo discovery of actively expressed fusion genes. Here we focus on the problem of detecting gene fusions from raw sequencing data, assembling the reads to define fusion transcripts and their associated breakpoints, and quantifying their abundances. Building on the pseudoalignment idea that simplifies and accelerates transcript quantification, we introduce a novel approach to fusion detection based on inspecting paired reads that cannot be pseudoaligned due to conflicting matches. The method and software, called pizzly, filters false positives, assembles new transcripts from the fusion reads, and reports candidate fusions. With pizzly, fusion detection from raw RNA-Seq reads can be performed in a matter of minutes, making the program suitable for the analysis of large cancer gene expression databases and for clinical use. pizzly is available at https://github.com/pmelsted/pizzly

bioinformatics↗

bioRxiv2017

Column subset selection for single-cell RNA-Seq clustering

The first step in the analysis of single-cell RNA sequencing (scRNA-Seq) is dimensionality reduction, which reduces noise and simplifies data visualization. However, techniques such as principal components analysis (PCA) fail to preserve non-negativity and sparsity structures present in the original matrices, and the coordinates of projected cells are not easily interpretable. Commonly used thresholding methods avoid those pitfalls, but ignore collinearity and covariance in the original matrix. We show that a deterministic column subset selection (DCSS) method possesses many of the favorable properties of PCA and common thresholding methods, while avoiding pitfalls from both. We derive new spectral bounds for DCSS. We apply DCSS to two measures of gene expression from two scRNA-Seq experiments with different clustering workflows, and compare to three thresholding methods. In each case study, the clusters based on the small subset of the complete gene expression profile selected by DCSS are similar to clusters produced from the full set. The resulting clusters are informative for cell type.

bioinformatics↗

bioRxiv2017

Association Mapping From Sequencing Reads Using K-mers

Genome wide association studies (GWAS) rely on microarrays, or more recently mapping of whole-genome sequencing reads, to genotype individuals. The reliance on prior sequencing of a reference genome for the organism on which the association study is to be performed limits the scope of association studies, and also precludes the identification of differences between cases and controls outside of the reference. We present an alignment free method for association studies that is based on counting k-mers in sequencing reads, testing for associations directly between k-mers and the trait of interest, and local assembly of the statistically significant k-mers to identify sequence differences. Results with simulated data and an analysis of the 1000 genomes data provide a proof of principle for the approach. In a pairwise comparison of the Toscani in Italia (TSI) and the Yoruba in Ibadan, Nigeria (YRI) populations we find that sequences identified by our method largely agree with results obtained using standard GWAS based on variant calling from mapped reads. However unlike standard GWAS, we find that our method identifies associations with structural variations and sites not present in the reference genome revealing sequences absent from the human reference genome. We also analyze data from the Bengali from Bangladesh (BEB) population to explore possible genetic basis of high rate of mortality due to cardiovascular diseases (CVD) among South Asians and find significant differences in frequencies of a number of non-synonymous variants in genes linked to CVDs between BEB and TSI samples, including the site rs1042034, which has been associated with higher risk of CVDs previously, and the nearby rs676210 in the Apolipoprotein B (ApoB) gene.\n\nAuthor SummaryWe present a method for associating regions in genomes to traits or diseases. The method is based on finding differences in frequencies of short strings of letters in sequencing reads and do not require reads to be aligned to a reference genome. This makes it applicable to study of organisms with no or incomplete reference genomes. We test our method with simulated data and sequencing data from the 1000 genomes project and find agreement with the conventional approach based on alignment to a reference genome. In addition, our method finds associations with sequences not in reference genomes and reveals sequences missing from the human reference genome. We also explore high rates of mortality due to cardiovascular diseases among South Asians and find prevalence of variations in genes associated with heart diseases in samples from the Bengali from Bangladesh population including one that has been reported to be associated with early onset of cardiovascular diseases.

bioinformatics↗

bioRxiv2017

Barcode identification for single cell genomics

Single-cell sequencing experiments use short DNA barcode tags to identify reads that originate from the same cell. In order to recover single-cell information from such experiments, reads must be grouped based on their barcode tag, a crucial processing step that precedes other computations. However, this step can be difficult due to high rates of mismatch and deletion errors that can afflict barcodes. Here we present an approach to identify and error-correct barcodes by traversing the de Bruijn graph of circularized barcode k-mers. This allows for assignment of reads to consensus fingerprints constructed from k-mers, and we show that for single-cell RNA-Seq this improves the recovery of accurate single-cell transcriptome estimates.

bioinformatics↗

bioRxiv2016

SWALO: scaffolding with assembly likelihood optimization

Scaffolding i.e. ordering and orienting contigs is an important step in genome assembly. We present a method for scaffolding based on likelihoods of genome assemblies. Generative models for sequencing are used to obtain maximum likelihood estimates of gaps between contigs and to estimate whether linking contigs into scaffolds would lead to an increase in the likelihood of the assembly. We then link contigs if they can be unambiguously joined or if the corresponding increase in likelihood is substantially greater than that of other possible joins of those contigs. The method is implemented in a tool called SO_SCPLOWWALOC_SCPLOW with approximations to make it efficient and applicable to large datasets. Analysis on real and simulated datasets reveals that it consistently makes more or similar number of correct joins as other scaffolders while linking very few contigs incorrectly, thus outperforming other scaffolders and demonstrating that substantial improvement in genome assembly may be achieved through the use of statistical models. SO_SCPLOWWALOC_SCPLOW is freely available for download at https://atifrahman.github.io/SWALO/.

bioinformatics↗