Biology subjects

Chu, C.

Publications and source records attributed to Chu, C..

4 recordsLinked to original sources

bioRxiv2018

CLADES: A Classification-based Machine Learning Method for Species Delimitation from Population Genetic Data

Species are considered to be the basic unit of ecological and evolutionary studies. Since multi-locus genomic data are becoming increasingly available, there has been considerable interests in the use of DNA sequence data to delimit species. In this paper, we show that machine learning can be used for species delimitation. There exists no species delimitation methods that are based on machine learning. Our method treats the species delimitation problem as a classification problem. It is a problem of identifying the category of a new observation on the basis of training data. Extensive simulation is first conducted over a broad range of evolutionary parameters for training purpose. Each pair of known populations are combined to form training samples with a label of \"same species\" or \"different species\". We use Support Vector Machine (SVM) to train a classifier using a set of summary statistics computed from training samples as features. The trained classifier can classify a test sample to two outcomes: \"same species\" or \"different species\". Given multi-locus genomic data of multiple related organisms or populations, our method (called CLADES) performs species delimitation by first classifying pairs of populations. CLADES then delimits species by maximizing the likelihood of species assignment for multiple populations. CLADES is evaluated through extensive simulation and also tested on real genetic data. We show that CLADES is both accurate and efficient for species delimitation when compared with existing methods. CLADES can be useful especially when existing methods have difficulty in delimitation, e.g. with short species divergence time and gene flow.

evolutionary biology↗

bioRxiv2017

OMSV enables accurate and comprehensive identification of large structural variations from nanochannel-based single-molecule optical maps

Human genomes contain structural variations (SVs) that are associated with various phenotypic variations and diseases. SV detection by sequencing is incomplete due to limited read length. Nanochannel-based optical mapping (OM) allows direct observation of SVs up to hundreds of kilo-bases in size on individual DNA molecules, making it a promising alternative technology for identifying large SVs. SV detection from optical maps is non-trivial due to complex types of error present in OM data, and no existing methods can simultaneously handle all these complex errors and the wide spectrum of SV types. Here we present a novel method, OMSV, for accurate and comprehensive identification of SVs from optical maps. OMSV detects both homozygous and heterozygous SVs, SVs of various types and sizes, and SVs with and without creating/destroying restriction sites. In an extensive series of tests based on real and simulated data, OMSV achieved both high sensitivity and specificity, with clear performance gains over the latest existing method. Applying OMSV to a human cell line, we identified hundreds of SVs >2kbp, with 65% of them missed by sequencing-based callers. Independent experimental validations confirmed the high accuracy of these SVs. We also demonstrate how OMSV can incorporate sequencing data to determine precise SV break points and novel sequences in the SVs not contained in the reference. We provide OMSV as open-source software to facilitate systematic studies of large SVs.

bioinformatics↗

bioRxiv2017

CircMarker: A Fast and Accurate Algorithm for Circular RNA Detection

While RNA is often created from linear splicing during transcription, recent studies have found that non-canonical splicing sometimes occurs. Non-canonical splicing joins 3 and 5 and forms the socalled circular RNA. It is now believed that circular RNA plays important biological roles such as affecting susceptibility in some diseases. within these few years, several experimental methods have been developed to enrich circular RNA while degrade linear RNA. Although several useful software tools for circRNA detection have been developed as well, these tools may miss many circular RNA. Also, existing tools are slow for large data because those tools often depend on reads mapping. In this paper, we present a new computational approach, named CircMarker, based on k-mers rather than reads mapping for circular RNA detection. CircMarker takes advantage of transcriptome annotation files to create k-mer table for circular RNA detection. Empirical results show that CircMarker outperforms existing tools in circular RNA detection on accuracy and efficiency in many simulated and real datasets. CircMarker can be downloaded from https://github.com/lxwgcool/CircMarker.

bioinformatics↗

bioRxiv2017

GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads

BackgroundClosing gaps in draft genomes is an important post processing step in genome assembly. It leads to more complete genomes, which benefits downstream genome analysis such as annotation and genotyping. Several tools have been developed for gap closing. However, these tools dont fully utilize the information contained in the sequence data. For example, while it is known that many gaps are caused by genomic repeats, existing tools often ignore many sequence reads that originate from a repeat-related gap.\n\nResultsIn this paper, we propose a new approach called GAPPadder for gap closing. The main advantage of GAPPadder is that it uses more information in sequence data for gap closing. In particular, GAPPadder finds and uses reads that originate from repeate-related gaps. We show that these repeat-associated reads are useful for gap closing, even though they are ignored by all existing tools. Other main features of GAPPadder include utilizing the information in sequence reads with different insert sizes and performing two-stage local assembly of gap sequences. We compare GAPPadder with GapCloser, GapFiller and Sealer on one bacterial genome, human chromosome 14 and the human whole genome with paired-end and mate-paired reads with both short and long insert sizes. Empirical results show that GAPPadder can close more gaps than these existing tools. Besides closing gaps on draft genomes assembled only from short sequence reads, GAPPadder can also be used to close gaps for draft genomes assembled with long reads. We show GAPPadder can close gaps on the bed bug genome and the Asian sea bass genome that are assembled partially and fully with long reads respectively. We also show GAPPadder is efficient in both time and memory usage. The software tool, GAPPadder, is available for download at https://github.com/Reedwarbler/GAPPadder.

bioinformatics↗