Biology subjects

Chu, J.

Publications and source records attributed to Chu, J..

6 recordsLinked to original sources

bioRxiv2018

Improving on hash-based probabilistic sequence classification using multiple spaced seeds and multi-index Bloom filters

Alignment-free classification of sequences against collections of sequences has enabled high-throughput processing of sequencing data in many bioinformatics analysis pipelines. Originally hash-table based, much work has been done to improve and reduce the memory requirement of indexing of k-mer sequences with probabilistic indexing strategies. These efforts have led to lower memory highly efficient indexes, but often lack sensitivity in the face of sequencing errors or polymorphism because they are k-mer based. To address this, we designed a new memory efficient data structure that can tolerate mismatches using multiple spaced seeds, called a multi-index Bloom Filter. Implemented as part of BioBloom Tools, we demonstrate our algorithm in two applications, read binning for targeted assembly and taxonomic read assignment. Our tool shows a higher sensitivity and specificity for read-binning than BWA MEM at an order of magnitude less time. For taxonomic classification, we show higher sensitivity than CLARK-S at an order of magnitude less time while using half the memory.

bioinformatics↗

bioRxiv2018

Strategies to improve photosynthetic nitrogen-use efficiency with no yield penalty: lessons from late-sown winter wheat

HighlightOptimal N allocation at several integration levels accounts for improved canopy PNUE while maintaining high grain yield in winter wheat\n\nAbstractImproving canopy photosynthetic nitrogen-use efficiency (PNUE) may maintain or even increase yield with reduced N input. In this study, later-sown winter wheat was studied to reveal the mechanism underlying improved canopy PNUE while maintaining high yield. N allocation at several levels was optimised in late-sown wheat plants. N content per plant increased. Increased N was allocated to the flag leaf and second leaf, and to ribulose-1, 5-bisphosphate carboxylase/oxygenase (Rubisco) in upper leaves. Constant or reduced N was allocated to leaf 3, leaf 4, and Rubisco in lower leaves. The specific green leaf area nitrogen (SLN) of upper leaves increased, while that of lower leaves remained unchanged or decreased. N allocation to the cell wall decreased in all leaves. As a result, the maximum carboxylation rate of upper leaves increased, and that of lower leaves remained constant or decreased. CO2 diffusion capacity was enhanced in all leaves. Outperformance by light-saturated net photosynthetic rate (Pmax) over SLN led to improved PNUE in upper leaves. Enhanced Pmax coupled with unchanged or decreased SLN resulted in improved PNUE in lower leaves. High yield was maintained because enhanced photosynthetic capacity at the leaf and whole plant levels compensated for reduced canopy leaf area.

cell biology↗

bioRxiv2018

ARKS: chromosome-scale scaffolding of human genomedrafts with linked read kmers

BackgroundThe long-range sequencing information captured by linked reads, such as those available from 10x Genomics (10xG), helps resolve genome sequence repeats, and yields accurate and contiguous draft genome assemblies. We introduce ARKS, an alignment-free linked read genome scaffolding methodology that uses linked reads to organize genome assemblies further into contiguous drafts. Our approach departs from other read alignment-dependent linked read scaffolders, including our own (ARCS), and uses a kmer-based mapping approach. The kmer mapping strategy has several advantages over read alignment methods, including better usability and faster processing, as it precludes the need for input sequence formatting and draft sequence assembly indexing. The reliance on kmers instead of read alignments for pairing sequences relaxes the workflow requirements, and drastically reduces the run time.\n\nResultsHere, we show how linked reads, when used in conjunction with Hi-C data for scaffolding, improve a draft human genome assembly of PacBio long-read data five-fold (baseline vs. ARKS NG50=4.6 vs. 23.1 Mbp, respectively). We also demonstrate how the method provides further improvements of a megabase-scale Supernova human genome assembly, which itself exclusively uses linked read data for assembly, with an execution speed six to nine times faster than competitive linked read scaffolders. Following ARKS scaffolding of a human genome 10xG Supernova assembly (of cell line NA12878), fewer than 9 scaffolds cover each chromosome, except the largest (chromosome 1, n=13).\n\nConclusionsARKS uses a kmer mapping strategy instead of linked read alignments to record and associate the barcode information needed to order and orient draft assembly sequences. The simplified workflow, when compared to that of our initial implementation, ARCS, markedly improves run time performances on experimental human genome datasets. Furthermore, ARKS utilizes barcoding information from linked reads to estimate gap size. It accomplishes this by modeling the relationship between known distances of a region within contigs and calculating associated Jaccard indices. ARKS has the potential to provide correct, chromosome-scale, genome assemblies, promptly. We expect ARKS to have broad utility in helping refine draft genomes.

bioinformatics↗

bioRxiv2018

Tigmint: Correcting Assembly Errors Using Linked Reads From Large Molecules

Genome sequencing yields the sequence of many short snippets of DNA (reads) from a genome. Genome assembly attempts to reconstruct the original genome from which these reads were derived. This task is difficult due to gaps and errors in the sequencing data, repetitive sequence in the underlying genome, and heterozygosity, and assembly errors are common. These misassemblies may be identified by comparing the sequencing data to the assembly, and by looking for discrepancies between the two. Once identified, these misassemblies may be corrected, improving the quality of the assembly. Although tools exist to identify and correct misassemblies using Illumina pair-end and mate-pair sequencing, no such tool yet exists that makes use of the long distance information of the large molecules provided by linked reads, such as those offered by the 10x Genomics Chromium platform. We have developed the tool Tigmint for this purpose. To demonstrate the effectiveness of Tigmint, we corrected assemblies of a human genome using short reads assembled with ABySS 2.0 and other assemblers. Tigmint reduced the number of misassemblies identified by QUAST in the ABySS assembly by 216 (27%). While scaffolding with ARCS alone more than doubled the scaffold NGA50 of the assembly from 3 to 8 Mbp, the combination of Tigmint and ARCS improved the scaffold NGA50 of the assembly over five-fold to 16.4 Mbp. This notable improvement in contiguity highlights the utility of assembly correction in refining assemblies. We demonstrate its usefulness in correcting the assemblies of multiple tools, as well as in using Chromium reads to correct and scaffold assemblies of long single-molecule sequencing. The source code of Tigmint is available for download from https://github.com/bcgsc/tigmint, and is distributed under the GNU GPL v3.0 license.

genomics↗

bioRxiv2017

ARCS: Assembly Roundup by Chromium Scaffolding

Sequencing of human genomes is now routine, and assembly of shotgun reads is increasingly feasible. However, assemblies often fail to inform about chromosome-scale structure due to lack of linkage information over long stretches of DNA - a shortcoming that is being addressed by new sequencing protocols, such as linked reads from 10X Genomics. Here we present ARCS, an application that utilizes the barcoding information contained in linked reads to further organize draft genomes into highly contiguous assemblies. We show how the contiguity of an ABySS H. sapiens genome assembly can be increased over six-fold using moderate coverage (25-fold) Chromium data. We expect ARCS to have broad utility in harnessing the barcoding information contained in Chromium data for connecting high-quality sequences in genome assembly drafts. Availability: http://www.bcgsc.ca/platform/bioinfo/software/arcs\n\nSupplementary information available online.

genomics↗

bioRxiv2016

Overlapping long sequence reads: Current innovations and challenges in developing sensitive, specific and scalable algorithms

Identifying overlaps between error-prone long reads, specifically those from Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PB), is essential for certain downstream applications, including error correction and de novo assembly. Though akin to the read-to-reference alignment problem, read-to-read overlap detection is a distinct problem that can benefit from specialized algorithms that perform efficiently and robustly on high error rate long reads. Here, we review the current state-of-the-art read-to-read overlap tools for error-prone long reads, including BLASR, DALIGNER, MHAP, GraphMap, and Minimap. These specialized bioinformatics tools differ not just in their algorithmic designs and methodology, but also in their robustness of performance on a variety of datasets, time and memory efficiency, and scalability. We highlight the algorithmic features of these tools, as well as their potential issues and biases when utilizing any particular method. We benchmarked these tools, tracking their resource needs and computational performance, and assessed the specificity and precision of each. The concepts surveyed may apply to future sequencing technologies, as scalability is becoming more relevant with increased sequencing throughput.\n\nContactcjustin@bcgsc.ca; ibirol@bcgsc.ca\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗