We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, ...producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
We describe a workflow for preprocessing of single-cell RNA-sequencing data that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near optimal in ...speed with a constant memory requirement providing scalability for arbitrarily large datasets. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses.
Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence ...collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license ( https://github.com/marbl/mash ).
Abstract
Summary
We describe a compression scheme for BUS files and an implementation of the algorithm in the BUStools software. Our compression algorithm yields smaller file sizes than gzip, at ...significantly faster compression and decompression speeds. We evaluated our algorithm on 533 BUS files from scRNA-seq experiments with a total size of 1TB. Our compression is 2.2× faster than the fastest gzip option 35% slower than the fastest zstd option and results in 1.5× smaller files than both methods. This amounts to an 8.3× reduction in the file size, resulting in a compressed size of 122GB for the dataset.
Availability and implementation
A complete description of the format is available at https://github.com/BUStools/BUSZ-format and an implementation at https://github.com/BUStools/bustools. The code to reproduce the results of this article is available at https://github.com/pmelsted/BUSZ_paper.
Memory consumption of de Bruijn graphs is often prohibitive. Most de Bruijn graph-based assemblers reduce the complexity by compacting paths into single vertices, but this is challenging as it ...requires the uncompacted de Bruijn graph to be available in memory. We present a parallel and memory-efficient algorithm enabling the direct construction of the compacted de Bruijn graph without producing the intermediate uncompacted graph. Bifrost features a broad range of functions, such as indexing, editing, and querying the graph, and includes a graph coloring method that maps each k-mer of the graph to the genomes it occurs in.Availability https://github.com/pmelsted/bifrost.
Analysis of sequence diversity in the human genome is fundamental for genetic studies. Structural variants (SVs) are frequently omitted in sequence analysis studies, although each has a relatively ...large impact on the genome. Here, we present GraphTyper2, which uses pangenome graphs to genotype SVs and small variants using short-reads. Comparison to the syndip benchmark dataset shows that our SV genotyping is sensitive and variant segregation in families demonstrates the accuracy of our approach. We demonstrate that incorporating public assembly data into our pipeline greatly improves sensitivity, particularly for large insertions. We validate 6,812 SVs on average per genome using long-read data of 41 Icelanders. We show that GraphTyper2 can simultaneously genotype tens of thousands of whole-genomes by characterizing 60 million small variants and half a million SVs in 49,962 Icelanders, including 80 thousand SVs with high-confidence.
Several applications in bioinformatics, such as genome assemblers and error corrections methods, rely on counting and keeping track of k-mers (substrings of length k). Histograms of k-mer frequencies ...can give valuable insight into the underlying distribution and indicate the error rate and genome size sampled in the sequencing experiment.
We present KmerStream, a streaming algorithm for estimating the number of distinct k-mers present in high-throughput sequencing data. The algorithm runs in time linear in the size of the input and the space requirement are logarithmic in the size of the input. We derive a simple model that allows us to estimate the error rate of the sequencing experiment, as well as the genome size, using only the aggregate statistics reported by KmerStream. As an application we show how KmerStream can be used to compute the error rate of a DNA sequencing experiment. We run KmerStream on a set of 2656 whole genome sequenced individuals and compare the error rate to quality values reported by the sequencing equipment. We discover that while the quality values alone are largely reliable as a predictor of error rate, there is considerable variability in the error rates between sequencing runs, even when accounting for reported quality values.
Advances in sequencing capacity have led to the generation of unprecedented amounts of genomic data. The processing of this data frequently leads to I/O bottlenecks, e. g. when analyzing a small ...genomic region across a large number of samples. The largest I/O burden is, however, often not imposed by the amount of data needed for the analysis but rather by index files that help retrieving this data. We have developed chopBAI, a program that can chop a BAM index (BAI) file into small pieces. The program outputs a list of BAI files each indexing a specified genomic interval. The output files are much smaller in size but maintain compatibility with existing software tools. We show how preprocessing BAI files with chopBAI can lead to a reduction of I/O by more than 95% during the analysis of 10 kb genomic regions, eventually enabling the joint analysis of more than 10 000 individuals.
The software is implemented in C ++, GPL licensed and available at http://github.com/DecodeGenetics/chopBAIContact:birte.kehr@decode.is.
We describe sleuth (http://pachterlab.github.io/sleuth), a method for the differential analysis of gene expression data that utilizes bootstrapping in conjunction with response error linear modeling ...to decouple biological variance from inferential variance. sleuth is implemented in an interactive shiny app that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of data from RNA-seq experiments.
Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our ...method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. 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.
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