Biology subjects

Chikhi, R.

Publications and source records attributed to Chikhi, R..

42 records · Page 3Linked to original sources

bioRxiv2019

yacrd and fpa: upstream tools for long-read genome assembly

MotivationGenome assembly is increasingly performed on long, uncorrected reads. Assembly quality may be degraded due to unfiltered chimeric reads; also, the storage of all read overlaps can take up to terabytes of disk space. ResultsWe introduce two tools, yacrd and fpa, preform respectively chimera removal, read scrubbing, and filter out spurious overlaps. We show that yacrd results in higher-quality assemblies and is one hundred times faster than the best available alternative. Availabilityhttps://github.com/natir/yacrd and https://github.com/natir/fpa Contactpierre.marijon@inria.fr Supplementary informationSupplementary data are available online.

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bioRxiv2018

Comparative assessment of long-read error-correction software applied to RNA-sequencing data

MotivationLong-read sequencing technologies offer promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However these technologies are currently hindered by high error rates that affect analyses such as the identification of isoforms, exon boundaries, open reading frames, and the creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error-correction of RNA-sequencing long reads remain limited.\n\nResultsIn this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error-correction metrics but also the effect of correction on gene families, isoform diversity, bias toward the major isoform, and splice site detection. We find that long read error-correction tools that were originally developed for DNA are also suitable for the correction of RNA-sequencing data, especially in terms of increasing base-pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error-correction tools should be used, depending on the application type.\n\nBenchmarking softwarehttps://gitlab.com/leoisl/LR_EC_analyser

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bioRxiv2017

RecoverY : K-mer based read classification for Y-chromosome specific sequencing and assembly

MotivationThe haploid mammalian Y chromosome is usually under-represented in genome assemblies due to high repeat content and low depth due to its haploid nature. One strategy to ameliorate the low coverage of Y sequences is to experimentally enrich Y-specific material before assembly. Since the enrichment process is imperfect, algorithms are needed to identify putative Y-specific reads prior to downstream assembly. A strategy that uses k-mer abundances to identify such reads was used to assemble the gorilla Y (Tomaszkiewicz et al 2016). However, the strategy required the manual setting of key parameters, a time-consuming process leading to sub-optimal assemblies.\n\nResultsWe develop a method, RecoverY, that selects Y-specific reads by automatically choosing the abundance level at which a k-mer is deemed to originate from the Y. This algorithm uses prior knowledge about the Y chromosome of a related species or known Y transcript sequences. We evaluate RecoverY on both simulated and real data, for human and gorilla, and investigate its robustness to important parameters. We show that RecoverY leads to a vastly superior assembly compared to alternate strategies of filtering the reads or contigs. Compared to the preliminary strategy used in Tomaszkiewicz et al (2016), we achieve a 33% improvement in assembly size and a 20% improvement in the NG50, demonstrating the power of automatic parameter selection.\n\nAvailabilityOur tool RecoverY is freely available at https://github.com/makovalab-psu/RecoverY\n\nContactkmakova@bx.psu.edu, pashadag@cse.psu.edu\n\nSupplementary informationAttached as an additional file.

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bioRxiv2017

Exhaustive capture of biological variation in RNA-seq data through k-mer decomposition

Each individual cell produces its own set of transcripts, which is the combined result of genetic variation, transcription regulation and post-transcriptional processing. Due to this combinatorial nature, obtaining the exhaustive set of full-length transcripts for a given species is a never-ending endeavor. Yet, each RNA deep sequencing experiment produces a variety of transcripts that depart from the reference transcriptome and should be properly identified. To address this challenge, we introduce a k-mer-based software protocol for capturing local RNA variation from a set of standard RNA-seq libraries, independently of a reference genome or transcriptome. Our software, called DE-kupl, analyzes k-mer contents and detects k-mers with differential abundance directly from the raw data files, prior to assembly or mapping. This enables to retrieve the virtually complete set of unannotated variation lying in an RNA-seq dataset. This variation is subsequently assigned to biological events such as differential lincRNAs, antisense RNAs, splice and polyadenylation variants, introns, expressed repeats, and SNV-harboring or exogenous RNA. We applied DE-kupl to public RNA-seq datasets, including an Epythelial-Mensenchymal Transition model and different human tissues. DE-kupl identified abundant novel events and showed excellent reproducibility when applied to independent deep sequencing experiments. DE-kupl is a new paradigm for analyzing differential RNA-seq data with no preconception on target events, which can also provide fresh insights into existing RNA-seq repositories.

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bioRxiv2017

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

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bioRxiv2016

AllSome Sequence Bloom Trees

The ubiquity of next generation sequencing has transformed the size and nature of many databases, pushing the boundaries of current indexing and searching methods. One particular example is a database of 2,652 human RNA-seq experiments uploaded to the Sequence Read Archive. Recently, Solomon and Kingsford proposed the Sequence Bloom Tree data structure and demonstrated how it can be used to accurately identify SRA samples that have a transcript of interest potentially expressed. In this paper, we propose an improvement called the AllSome Sequence Bloom Tree. Results show that our new data structure significantly improves performance, reducing the tree construction time by 52.7% and query time by 39 - 85%, with a price of up to 3x memory consumption during queries. Notably, it can query a batch of 198,074 queries in under 8 hours (compared to around two days previously) and a whole set of k-mers from a sequencing experiment (about 27 mil k-mers) in under 11 minutes.

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