Biology subjects

Ma, L.

Publications and source records attributed to Ma, L..

22 records · Page 2Linked to original sources

bioRxiv2017

5-Hydroxymethylcytosine signatures in cell-free DNA provide information about tumor types and stages

5-Hydroxymethylcytosine (5hmC) is an important mammalian DNA epigenetic modification that has been linked to gene regulation and cancer pathogenesis. Here we explored the diagnostic potential of 5hmC in circulating cell-free DNA (cfDNA) using a sensitive chemical labeling-based low-input shotgun sequencing approach. We sequenced cell-free 5hmC from 49 patients of seven different cancer types and found distinct features that could be used to predict cancer types and stages with high accuracy. Specifically, we discovered that lung cancer leads to a progressive global loss of 5hmC in cfDNA, whereas hepatocellular carcinoma and pancreatic cancer lead to disease-specific changes in the cell-free hydroxymethylome. Our proof of principle results suggest that cell-free 5hmC signatures may potentially be used not only to identify cancer types but also to track tumor stage in some cancers.\n\nOne Sentence SummaryAnalyzing the epigenetic modification 5-hydroxymethylcysoine in circulating cell-free DNA reveals tumor tissue of origin and stages for cancer diagnostics.

genomics↗

bioRxiv2017

Systems Biology Analysis Using A Genome-Scale Metabolic Model Shows That Phosphine Triggers Global Metabolic Suppression In A Resistant Strain Of C. elegans

BackgroundPest insects are increasingly resistant to phosphine gas, which is used globally to protect grain reserves. The enzyme dihydrolipoamide dehydrogenase (DLD) is a phosphine resistance factor and participates in four key steps of core metabolism, making it a potential central metabolic regulator.\n\nResultsHere we used microarray data and NMR-based metabolomics to characterize the phosphine response of wild-type C. elegans and the phosphine-resistant strain dld-1(wr4) which has a partial loss-of-function mutation in the gene for DLD. In addition, we have constructed CeCon, a C. elegans genome-scale metabolic model to facilitate integration of gene expression and metabolomics data.\n\nConclusionsThe resulting systems biology analysis is consistent with the hypothesis that adaptation to a hypometabolic state is the most prominent mechanism of phosphine resistance in this nematode strain. The involvement of DLD in regulating and creating hypometabolic adaptation has implications for other biological phenomena involving hypometabolism, such as reperfusion injury and metabolic resistance.

systems biology↗

bioRxiv2017

BGDMdocker: an workflow base on Docker for analysis and visualization pan-genome and biosynthetic gene clusters of Bacterial

MotivationAt present Docker technology has received increasing level of attention throughout the bioinformatics community. However, its implementation details have not yet been mastered by most biologists and applied widely in biological researches. In order to popularizing this technology in the bioinformatics and sufficiently use plenty of public resources of bioinformatics tools (Dockerfile and image of scommunity, officially and privately) in Docker Hub Registry and other Docker sources based on Docker, we introduced full and accurate instance of a bioinformatics workflow based on Docker to analyse and visualize pan-genome and biosynthetic gene clusters of a bacteria in this article, provided the solutions for mining bioinformatics big data from various public biology databases. You could be guided step-by-step through the workflow process from docker file to build up your own images and run an container fast creating an workflow.\n\nResultsWe presented a BGDMdocker (bacterial genome data mining docker-based) workflow based on docker. The workflow consists of three integrated toolkits, Prokka v1.11, panX, and antiSMASH3.0. The dependencies were all written in Dockerfile, to build docker image and run container for analysing pan-genome of total 44 Bacillus amyloliquefaciens strains, which were retrieved from public? database. The pan-genome totally includes 172,432 gene, 2,306 Core gene cluster. The visualized pan-genomic data such as alignment, phylogenetic trees, maps mutations within that cluster to the branches of the tree, infers loss and gain of genes on the core-genome phylogeny for each gene cluster were presented. Besides, 997 known (MIBiG database) and 553 unknown (antiSMASH-predicted clusters and Pfam database) genes of biosynthesis gene clusters types and orthologous groups were mined in all strains. This workflow could also be used for other species pan-genome analysis and visualization. The display of visual data can completely duplicated as well as done in this paper. All result data and relevant tools and files can be downloaded from our website with no need to register. The pan-genome and biosynthetic gene clusters analysis and visualization can be fully reusable immediately in different computing platforms (Linux, Windows, Mac and deployed in the cloud), achieved cross platform deployment flexibility, rapid development integrated software package.\n\nAvailability and implementationBGDMdocker is available at http://42.96.173.25/bapgd/ and the source code under GPL license is available at https://github.com/cgwyx/debian_prokka_panx_antismash_biodocker.\n\nContactchenggongwyx@foxmail.com\n\nSupplementary informationSupplementary data are available at biorxiv online.

bioinformatics↗

bioRxiv2016

Statistical testing and power analysis for brain-wide association study

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression [Cheng et al., 2015a,b, 2016], the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method considerably reduces the computational complexity of a permutation-or simulation-based approach, thus, it can efficiently tackle large datasets with ultra-high resolution images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods failed. A software package is available at https://github.com/weikanggong/BWAS.

neuroscience↗