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Tang, C.

Publications and source records attributed to Tang, C..

146 records · Page 9Linked to original sources

AASRA: An Anchor Alignment-Based Small RNA Annotation Pipeline

SncRNA-Seq has become a routine for sncRNA profiling; however, software packages currently available are either exclusively for miRNA or piRNA annotation (e.g., miRDeep, miRanalyzer, Shortstack, PIANO), or for direct mapping of the sequence reads to the genome (e.g., Bowtie 2, SOAP and BWA), which tend to generate inaccurate counting due to repetitive matches to the genome or sncRNA homologs. Moreover, novel sncRNA variants in the sequencing reads, including those bearing small overhangs or internal insertions, deletions or mutations, are totally excluded from counting by these algorithms, leading to potential quantification bias. To overcome these problems, a comprehensive software package that can annotate all known small RNA species with adjustable tolerance towards small mismatches is needed. AASRA is based on our unique anchor alignment algorithm, which not only avoids repetitive or ambiguous counting, but also distinguishes mature miRNA from precursor miRNA reads. Compared to all existing pipelines for small RNA annotation, AASRA is superior in the following aspects: 1) AASRA can annotate all known sncRNA species simultaneously with the capability of distinguishing mature and precursor miRNAs; 2) AASRA can identify and allow for inclusion of sncRNA variants with small overhangs and/or internal insertions/deletions into the final counts; 3) AASRA is the fastest among all small RNA annotation pipelines tested. AASRA represents an all-in-one sncRNA annotation pipeline, which allows for high-speed, simultaneous annotation of all known sncRNA species with the capability to distinguish mature from precursor miRNAs, and to identify novel sncRNA variants in the sncRNA-Seq sequencing reads.\n\nAvailability and ImplementationThe AASRA software is freely available at https://github.com/biogramming/AASRA.

bioinformatics↗

Optimal Growth Of Microbes On Mixed Carbon Sources

A classic problem in microbiology is that bacteria display two types of growth behavior when cultured on a mixture of two carbon sources: in certain mixtures the bacteria consume the two carbon sources sequentially (diauxie) and in other mixtures the bacteria consume both sources simultaneously (co-utilization). The search for the molecular mechanism of diauxie led to the discovery of the lac operon and gene regulation in general. However, why microbes would bother to have different strategies of taking up nutrients remained a mystery. Here we show that diauxie versus co-utilization can be understood from the topological features of the metabolic network. A model of optimal allocation of protein resources to achieve maximum growth quantitatively explains why and how the cell makes the choice when facing multiple carbon sources. Our work solves a long-standing puzzle and sheds light on microbes optimal growth in different nutrient conditions.

systems biology↗