Biology subjects

Sikic, M.

Publications and source records attributed to Sikic, M..

22 records · Page 2Linked to original sources

bioRxiv2019

Yet another de novo genome assembler

Advances in sequencing technologies have pushed the limits of genome assemblies beyond imagination. The sheer amount of long read data that is being generated enables the assembly for even the largest and most complex organism for which efficient algorithms are needed. We present a new tool, called Ra, for de novo genome assembly of long uncorrected reads. It is a fast and memory friendly assembler based on sequence classification and assembly graphs, developed with large genomes in mind. It is freely available at https://github.com/lbcb-sci/ra.\n\nThis work has been supported in part by the Croatian Science Foundation under the project Single genome and metagenome assembly (IP-2018-01-5886), and in part by the European Regional Development Fund under the grant KK.01.1.1.01.0009 (DATACROSS). In addition, M.[S]. is partly supported by funding from A*STAR, Singapore.

bioinformatics↗

bioRxiv2018

Nanopore sequencing enables high-resolution analysis of resistance determinants and mobile elements in the human gut microbiome

The analysis of information rich whole-metagenome datasets acquired from complex microbial communities is often restricted by the fragmented nature of assembly from short-read sequencing. The availability of long-reads from third-generation sequencing technologies (e.g. PacBio or Oxford Nanopore) can help improve assembly quality in principle, but high error rates and low throughput have limited their application in metagenomics. In this work, we describe the first hybrid metagenomic assembler which combines the advantages of short and long-read technologies, providing an order of magnitude improvement in contiguity compared to short read assemblies, and high base-pair level accuracy. The proposed approach (OPERA-MS) integrates a novel assembly-based metagenome clustering technique with an exact scaffolding algorithm that can efficiently assemble repeat rich sequences. Based on evaluations with defined in vitro communities and virtual gut microbiomes, we show that it is possible to assemble near complete genomes from metagenomes with as little as 9x long read coverage, thus enabling high quality assembly of lowly abundant species (<1%). Furthermore, OPERA-MSs fine-grained clustering is able to deconvolute and assemble multiple genomes of the same species in a single sample, allowing us to study the complex dynamics of the human microbiome at the sub-species level. Applying nanopore sequencing to gut metagenomes of patients undergoing antibiotic treatment, we show that long reads can be obtained from stool samples in clinical studies to produce more meaningful metagenomic assemblies (up to 200x improvement over short-read assemblies), including the closed assembly of >80 putative plasmid/phage sequences and a 263kbp jumbo phage. Our results highlight that high-quality hybrid assemblies provide an unprecedented view of the gut resistome in these patients, including strain dynamics and identification of novel plasmid sequences.

bioinformatics↗

bioRxiv2017

RNA Transcriptome Mapping with GraphMap

Next generation sequencing technologies have made RNA sequencing widely accessible and applicable in many areas of research. In recent years, 3rd generation sequencing technologies have matured and are slowly replacing NGS for DNA sequencing. This paper presents a novel tool for RNA mapping guided by gene annotations. The tool is an adapted version of a previously developed DNA mapper - GraphMap, tailored for third generation sequencing data, such as those produced by Pacific Biosciences or Oxford Nanopore Technologies devices. It uses gene annotations to generate a transcriptome, uses a DNA mapping algorithm to map reads to the transcriptome, and finally transforms the mappings back to genome coordinates. Modified version of GraphMap is compared on several synthetic datasets to the state-of-the-art RNAseq mappers enabled to work with third generation sequencing data. The results show that our tool outperforms other tools in general mapping quality.

bioinformatics↗

bioRxiv2017

Evaluation of tools for long read RNA-seq splice-aware alignment

MotivationHigh-throughput sequencing has transformed the study of gene expression levels through RNA-seq, a technique that is now routinely used by various fields, such as genetic research or diagnostics. The advent of third generation sequencing technologies providing significantly longer reads opens up new possibilities. However, the high error rates common to these technologies set new bioinformatics challenges for the gapped alignment of reads to their genomic origin. In this study, we have explored how currently available RNA-seq splice-aware alignment tools cope with increased read lengths and error rates. All tested tools were initially developed for short NGS reads, but some have claimed support for long PacBio or even ONT MinION reads.\n\nResultsThe tools were tested on synthetic and real datasets from the PacBio and ONT MinION technologies, and both alignment quality and resource usage were compared across tools. The effect of error correction of long reads was explored, both using self-correction and correction with an external short reads dataset. A tool was developed for evaluating RNA-seq alignment results. This tool can be used to compare the alignment of simulated reads to their genomic origin, or to compare the alignment of real reads to a set of annotated transcripts.\n\nOur tests show that while some RNA-seq aligners were unable to cope with long error-prone reads, others produced overall good results. We further show that alignment accuracy can be improved using error-corrected reads.\n\nAvailabilityhttps://github.com/kkrizanovic/RNAseqEval\n\nContactmile.sikic@fer.hr

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