Biology subjects

Li, C.

Publications and source records attributed to Li, C..

1,107 records · Page 62Linked to original sources

bioRxiv2017

Fast and simple analysis of MiSeq amplicon sequencing data with MetaAmp

Microbial community profiling by barcoded 16S rRNA gene amplicon sequencing currently has many applications in microbial ecology. The low costs of the parallel sequencing of multiplexed samples, combined with the relative ease of data processing and interpretation (compared to shotgun metagenomes) have made this an entry-level approach. Here we present the MetaAmp pipeline for processing of SSU rRNA gene and other non-coding or protein-coding amplicon sequencing data by investigators that are inexperienced with bioinformatics procedures. It accepts single-end or paired-end sequences in fasta or fastq format from various sequencing platforms. It includes read quality control, and merging of forward and reverse reads of paired-end reads. It makes use of UPARSE, Mothur, and the SILVA database for clustering, removal of chimeric reads, taxonomic classification and generation of diversity metrics. The pipeline has been validated with a mock community of known composition. MetaAmp provides a convenient web interface as well as command line interface. It is freely available at: http://ebg.ucalgary.ca/metaamp. Since its launch two years ago, MetaAmp has been used >2,800 times, by many users worldwide.

bioinformatics↗

bioRxiv2017

Assessing species biomass contributions in microbial communities via metaproteomics

Assessment of microbial community composition is the cornerstone of microbial ecology. Microbial community composition can be analyzed by quantifying cell numbers or by quantifying biomass for individual populations. However, as cell volumes can differ by orders of magnitude, these two approaches yield vastly different results. Methods for quantifying cell numbers are already available (e.g. fluorescence in situ hybridization, 16S rRNA gene amplicon sequencing), yet methods for assessing community composition in terms of biomass are lacking.\n\nWe developed metaproteomics based methods for assessing microbial community composition using protein abundance as a measure for biomass contributions of individual populations. We optimized the accuracy and sensitivity of the method using artificially assembled microbial communities and found that it is less prone to some of the biases found in sequencing-based methods. We applied the method using communities from two different environments, microbial mats from two alkaline soda lakes and saliva from multiple individuals.

microbiology↗

bioRxiv2017

ConsensusDriver Improves Upon Individual Algorithms For Predicting Driver Alterations In Different Cancer Types And Individual Patients -- A Toolbox For Precision Oncology

BackgroundIn recent years, several large-scale cancer genomics studies have helped generate detailed molecular profiling datasets for many cancer types and thousands of patients. These datasets provide a unique resource for studying cancer driver prediction methods and their utility for precision oncology, both to predict driver genetic alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nMethodsWe performed the most comprehensive assessment to date of 18 driver gene prediction methods, on more than 3,400 tumour samples, from 15 cancer types, to determine their suitability in guiding precision medicine efforts. These methods have diverse approaches, which can be classified into five categories: functional impact on proteins in general (FI) or specific to cancer (FIC), cohort-based analysis for recurrent mutations (CBA), mutations with expression correlation (MEC) and methods that use gene interaction network-based analysis (INA).\n\nResultsThe performance of driver prediction methods varies considerably, with concordance with a gold-standard varying from 9% to 68%. FI methods show relatively poor performance (concordance <22%) while CBA methods provide conservative results, but require large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provide the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of drivers, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity). This tool can be applied to predict driver alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nConclusionExisting cancer driver prediction methods are based on very different assumptions and each of them can only detect a particular subset of driver events. Consensus-based methods, like ConsensusDriver, are thus a promising approach to harness the strengths of different driver prediction paradigms.

bioinformatics↗

bioRxiv2017

Gut Microbiota Composition Related With Clostridium difficile-Positive Diarrhea And C. Difficile Type (A+B+, A-B+, And A-B-) In ICU Hospitalized Patients

BackgroundGut microbiota composition of intensive care unit (ICU) patients suffering from Clostridium difficile-positive diarrhea (CDpD) is still poorly understood. This study aims to use 16S rDNA (and metagenome) sequencing to compare the microbiota composition of 58 (and 5) ICU patients with CDpD (CDpD group), 33 (and 4) ICU patients with C. difficile negative diarrhea (CDnD group), and 21 (and 5) healthy control subjects (control group), as well as CDpD patients in A+B+ (N=34; A/B: C. difficile TcdA/B), A-B+ (N=7), and A-B- (N=17) subgroups. For 16S rDNA data, OTU clustering (tool: UPARSE), taxonomic assignment (tool: RDP classifier), -diversity and {beta}-diversity analyses (tool: QIIME) were conducted. For metagenome data, metagenome assembly (tool: SOAP), gene calling (tools: MetaGeneMark, CD-HIT, and SoapAligner), unigene alignment (tool: DIAMOND), taxon difference analysis (tool: Metastats), and gene annotation (tool: DIAMOND) were performed.\n\nResultsThe microbial diversity of CDpD group was lower than that of CDnD and control groups. The abundances of 10 taxa (e.g. Deferribacteres, Cryptomycota, Acetothermia) in CDpD group were significantly higher than that in CDnD group. The abundances of Saccharomycetes and Clostridia were significantly lower in CDpD in comparison with control. A+B+, A-B+ and A-B- subgroups couldnt be separated in principal component analysis, while some taxa are significantly different between A+B+ and A-B- subgroups.\n\nConclusionCDpD might relate to the decrease of beneficial taxa (i.e. Saccharomycetes and Clostridia) and the increase of harmful taxa (e.g. Deferribacteres, Cryptomycota, Acetothermia) in gut microbiota in ICU patients. C. difficile type might be slightly associated with gut microbiota composition.

molecular biology↗

bioRxiv2017

OGT binds a conserved C-terminal domain of TET1 to regulate TET1 activity and function in development

Mammalian TET enzymes oxidize 5-methylcytosine to 5-hydroxymethylcytosine and higher oxidized derivatives. TETs are targets of the enzyme OGT, which post-translationally modifies intracellular proteins in response to cellular nutrient status. The biological implications of the OGT-TET interaction have not been thoroughly explored. Here, we show for the first time that modification of TET1 by OGT enhances its activity in vitro. We identify a previously uncharacterized domain of TET1 responsible for binding to OGT and report a point mutation that disrupts the OGT-TET1 interaction. Finally, we show that the interaction between TET1 and OGT is necessary for TET1 to rescue tet mutant zebrafish hematopoetic stem cell formation, suggesting that OGT promotes TET1s function in development. Our results demonstrate regulation of TET activity by OGT in vitro and in vivo. These results link metabolism and epigenetic control, which may be relevant to the developmental and disease processes regulated by these two enzymes.

biochemistry↗

bioRxiv2017

CiliaCarta: An Integrated And Validated Compendium Of Ciliary Genes

The cilium is an essential organelle at the surface of most mammalian cells whose dysfunction causes a wide range of genetic diseases collectively called ciliopathies. The current rate at which new ciliopathy genes are identified suggests that many ciliary components remain undiscovered. We generated and rigorously analyzed genomic, proteomic, transcriptomic and evolutionary data and systematically integrated these using Bayesian statistics into a predictive score for ciliary function. This resulted in 285 candidate ciliary genes. We found experimental evidence of ciliary associations for 24 out of 36 analyzed candidate proteins. In addition, we show that OSCP1, which has previously been implicated in two distinct non-ciliary functions, causes a cilium dysfunction phenotype when depleted in zebrafish. The candidate list forms the basis of CiliaCarta, a comprehensive ciliary compendium covering 836 genes. The resource can be used to objectively prioritize candidate genes in whole exome or genome sequencing of ciliopathy patients and can be accessed at http://bioinformatics.bio.uu.nl/john/syscilia/ciliacarta/.

bioinformatics↗

bioRxiv2017

The Nuclear And Mitochondrial Genomes Of The Facultatively Eusocial Orchid Bee Euglossa dilemma

Bees provide indispensable pollination services to both agricultural crops and wild plant populations, and several species of bees have become important models for the study of learning and memory, plant-insect interactions and social behavior. Orchid bees (Apidae: Euglossini) are especially important to the fields of pollination ecology, evolution, and species conservation. Here we report the nuclear and mitochondrial genome sequences of the orchid bee Euglossa dilemma Bembe & Eltz. Euglossa dilemma was selected because it is widely distributed, highly abundant, and it was recently naturalized in the southeastern United States. We provide a high-quality assembly of the 3.3 giga-base genome, and an official gene set of 15,904 gene annotations. We find high conservation of gene synteny with the honey bee throughout 80 million years of divergence time. This genomic resource represents the first draft genome of the orchid bee genus Euglossa, and the first draft orchid bee mitochondrial genome, thus representing a valuable resource to the research community.

genomics↗

bioRxiv2016

Premature termination codons signaled targeted repair of frameshift mutation by endogenous RNA-directed gene editing

The molecular mechanisms for repairing DNA damages and point mutations have been well understood but it remains unclear how a frameshift mutation is repaired. Here we report that frameshift reversion occurs in E. coli more frequently than expected and appears to be a targeted gene repair signaled by premature termination codons (PTCs), producing high-level variations in the repaired genes. Genome resequencing shows that the revertant genome is highly stable, and the single-molecule variations in the repaired genes are derived from RNA editing. A multi-omics analysis shows that the expression levels change greatly in most the DNA and RNA manipulating genes. DNA replication, transcription, RNA editing, RNA degradation, nucleotide excision repair, mismatch repair, and homologous recombination were upregulated in the frameshift or revertant, but the base excision repair was not. Moreover, genes and transposons in a duplicate region silenced in wild type E. coli were activated in the frameshift. Finally, we propose a nonsense-mediated gene revising (NMGR) model for frame repair, which also acts as a driving force for molecular evolution. In essence, nonsense mRNAs are recognized, edited, and transported to template the repair of the coding gene by RNA-directed DNA repair, nucleotide excision, mismatch repair, and homologous recombination. Thanks to NMGR, the mutation rate temporarily rises in a frameshift gene, bringing genetic diversity while repairing the frameshift mutation and accelerating the evolution process without a high mutation rate in the genome.

genetics↗

bioRxiv2016

Segregation between the parietal memory network and the default mode network: Effects of spatial smoothing and model order in ICA

A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network (DMN), its function has been associated with memory processing, and it has been referred to as the parietal memory network (PMN). Independent component analysis (ICA) is the most common data-driven method of extracting PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determination in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high-order model across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.

neuroscience↗