Biology subjects

Chu, J.

Publications and source records attributed to Chu, J..

64 records · Page 4Linked to original sources

bioRxiv2019

RNA-Bloom provides lightweight reference-free transcriptome assembly for single cells

We present RNA-Bloom, a de novo RNA-seq assembly algorithm that leverages the rich information content in single-cell transcriptome sequencing (scRNA-seq) data to reconstruct cell-specific isoforms. We benchmark RNA-Blooms performance against leading bulk RNA-seq assembly approaches, and illustrate its utility in detecting cell-specific gene fusion events using sequencing data from HiSeq-4000 and BGISEQ-500 platforms. We expect RNA-Bloom to boost the utility of scRNA-seq data, expanding what is informatically accessible now.

bioinformatics↗

bioRxiv2019

ALI-1, candidate gene of B1 locus, is associated with awn length and grain weight in common wheat

Awn plays a vital role in the photosynthesis, grain production and drought tolerance of common wheat; however, works on the systematic identification or cloning of genes controlling wheat awn length (AL) were seldom reported. Here, we conducted the Genome-wide association study (GWAS) in 364 wheat accessions and identified 25 loci involved in the AL, including dominant awn suppressors B1, B2 and four homologs of awn controlling genes in rice and barley. Furthermore, the B1 locus was mapped to a 125-kb physical interval harboring two genes on chromosome 5AL through map-based cloning. As the candidate gene for B1 locus, a C2H2 zinc finger gene Awn Length Inhibitor 1 (ALI-1) expressed predominantly in the developing spike of awnless individuals and suppresses downstream genes transcriptionally. ALI-1 reduces cytokinin content and simultaneously restrains cytokinin signal transduction, which leads to a stagnation of cell proliferation and reduction of cell number in awn. Noteworthily, ali-1 was the first awn controlling locus that observed increasing grain length in wheat, which is a valuable supplemental attribution of awn on grain weight besides photosynthesis. Thus, ALI-1 pleiotropically regulates awn and grain development, and this work provides a strategy to achieve improved grain yield and address future extreme climate.\n\nHighlightALI-1, candidate gene of awn suppressing B1 locus, associates with awn length and grain length, providing a reacquaint of the effect of wheat awn on grain production.

plant biology↗

bioRxiv2019

Volumetric two-photon imaging in live cells and embryos via axially gradient excitation

Two-photon microscopy(TPM) that features subcellular resolution, intrinsic optical sectioning ability, and deep penetration in sample is a powerful tool of bioimaging. However, the process of layer-by-layer scanning to form a 3D image inherently limits the volumetric imaging speed and significantly increases the phototoxicity. Here we develop a gradient TPM technique that enables rapid volumetric imaging by only acquiring two 2D images. By sequentially exciting the specimen with two axially elongated two-photon beams with complementary gradient intensities, the axial positions of fluorophores can be decoded from the intensity ratio of the paired images. We achieve an axial localization accuracy of 0.728 {+/-} 0.657 m, which is sufficient for rapid 3D subcellular imaging. We demonstrate the flexibility of the gradient TPM on a variety of sparsely labelled samples, including bead phantoms, mouse brain tissues, live macrophages and live nematode embryos. The results show that, compared with conventional TPM, the 3D imaging speed increases 6 folds while the photobleaching and photodamage are extremely reduced.

bioengineering↗

bioRxiv2018

Magnetoelectric materials for miniature, wireless neural stimulation at therapeutic frequencies

A fundamental challenge for bioelectronics is to deliver power to miniature devices inside the body. Wires are common failure points and limit device placement. On the other hand, wireless power by electromagnetic or ultrasound waves must overcome absorption by the body and impedance mismatches between air, bone, and tissue. In contrast, magnetic fields suffer little absorption by the body or differences in impedance at interfaces between air, bone, and tissue. These advantages have led to magnetically-powered stimulators based on induction or magnetothermal effects. However, fundamental limitations in these power transfer technologies have prevented miniature magnetically-powered stimulators from applications in many therapies and disease models because they do not operate in clinical "high-frequency" ranges above 50 Hz. Here we show that magnetoelectric materials - applied in bioelectronic devices - enable miniature magnetically-powered neural stimulators that can operate up to clinically-relevant high-frequencies. As an example, we show that ME neural stimulators can effectively treat the symptoms of a hemi-Parkinsons disease model in freely behaving rodents. We further demonstrate that ME-powered devices can be miniaturized to mm-sized devices, fully implanted, and wirelessly powered in freely behaving rodents. These results suggest that ME materials are an excellent candidate for wireless power delivery that will enable miniature bioelectronics for both clinical and research applications.

bioengineering↗

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↗