RNF: a general framework to evaluate NGS read mappers

Aligning reads to a reference sequence is a fundamental step in numerous bioinformatics pipelines. As a consequence, the sensitivity and precision of the mapping tool, applied with certain parameters to certain data, can critically affect the accuracy of produced results (e.g., in variant calling applications). Therefore, there has been an increasing demand of methods for comparing mappers and for measuring effects of their parameters. Read simulators combined with alignment evaluation tools provide the most straightforward way to evaluate and compare mappers. Simulation of reads is accompanied by information about their positions in the source genome. This information is then used to evaluate alignments produced by the mapper. Finally, reports containing statistics of successful read alignments are created. In default of standards for encoding read origins, every evaluation tool has to be made explicitly compatible with the simulator used to generate reads. In order to solve this obstacle, we have created a generic format RNF (Read Naming Format) for assigning read names with encoded information about original positions. Futhermore, we have developed an associated software package RNF containing two principal components. MIShmash applies one of popular read simulating tools (among DwgSim, Art, Mason, CuReSim etc.) and transforms the generated reads into RNF format. LAVEnder evaluates then a given read mapper using simulated reads in RNF format. A special attention is payed to mapping qualities that serve for parametrization of ROC curves, and to evaluation of the effect of read sample contamination

Distribution of ammonium nitrate as nitrogen containing nutrient for in-situ biodegradation by means of electrokinetics

In-situ bioremediation is a technology, which has recently gained the attention of specialists for the clean-up of hydrocarbons. Organic chemicals are persistent and hard to deal with, because they are usually present in three forms: dissolved into groundwater, as free product over the groundwater surface, and adsorbed onto soil particles. The requirements for the bioremediation process to occur are the availability of microorganisms, a biodegradable pollutant, an electron acceptor, and nutrients. The shortage of nutrients in an available form for the microorganisms is very often a limiting factor for successful bioremediation in-situ. The main difficulties for the supply of nutrients usually come from the low permeability of soils. The feasibility of the application of electrokinetic processes, and, more specifically, the induced electroosmotic flow, for achievement of uniform distribution of nutrients for in-situ bioremediation in a natural clayey silt was investigated. Three different concentrations of ammonium nitrate solution were used. The experiment showed the efficiency of the electrokinetic method for supplying nutrients in a low permeability soil, especially for distribution of solutions with intermediate (1000 mg/L) concentrations. An advantage of the method is the prevention of the leaching of nitrates through the controlled electroosmotic flow

Dynamic read mapping and online consensus calling for better variant detection

Variant detection from high-throughput sequencing data is an essential step in identification of alleles involved in complex diseases and cancer. To deal with these massive data, elaborated sequence analysis pipelines are employed. A core component of such pipelines is a read mapping module whose accuracy strongly affects the quality of resulting variant calls.We propose a dynamic read mapping approach that significantly improves read alignment accuracy. The general idea of dynamic mapping is to continuously update the reference sequence on the basis of previously computed read alignments. Even though this concept already appeared in the literature, we believe that our work provides the first comprehensive analysis of this approach.To evaluate the benefit of dynamic mapping, we developed a software pipeline (http://github.com/karel-brinda/dymas) that mimics different dynamic mapping scenarios. The pipeline was applied to compare dynamic mapping with the conventional static mapping and, on the other hand, with the so-called iterative referencing – a computationally expensive procedure computing an optimal modification of the reference that maximizes the overall quality of all alignments. We conclude that in all alternatives, dynamic mapping results in a much better accuracy than static mapping, approaching the accuracy of iterative referencing.To correct the reference sequence in the course of dynamic mapping, we developed an online consensus caller named Ococo (http://github.com/karel-brinda/ococo). Ococo is the first consensus caller capable to process input reads in the online fashion.Finally, we provide conclusions about the feasibility of dynamic mapping and discuss main obstacles that have to be overcome to implement it. We also review a wide range of possible applications of dynamic mapping with a special emphasis on variant detection

Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data

Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks

Analysis of Somatic Alterations in Cancer Genome: From SNP Arrays to Next Generation Sequencing

International audienceIn this chapter we consider basic hypothesis, problem statements and technological and computa- tional solutions for analysis of copy number alterations in tumor genomes. We provide a data mining tech- nique (based on the GAP method described in (Popova et al., 2009)) which allows extraction of absolute copy numbers and allelic contents from the whole genome copy number variation and allelic imbalance profiles obtained by SNP arrays or NGS

SVDetect: a tool to identify genomic structural variations from paired-end and mate-pair sequencing data

Summary: We present SVDetect, a program designed to identify genomic structural variations from paired-end and mate-pair next-generation sequencing data produced by the Illumina GA and ABI SOLiD platforms. Applying both sliding-window and clustering strategies, we use anomalously mapped read pairs provided by current short read aligners to localize genomic rearrangements and classify them according to their type, e.g. large insertions–deletions, inversions, duplications and balanced or unbalanced inter-chromosomal translocations. SVDetect outputs predicted structural variants in various file formats for appropriate graphical visualization

Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization

Summary: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs

De novo motif identification improves the accuracy of predicting transcription factor binding sites in ChIP-Seq data analysis

Dramatic progress in the development of next-generation sequencing technologies has enabled accurate genome-wide characterization of the binding sites of DNA-associated proteins. This technique, baptized as ChIP-Seq, uses a combination of chromatin immunoprecipitation and massively parallel DNA sequencing. Other published tools that predict binding sites from ChIP-Seq data use only positional information of mapped reads. In contrast, our algorithm MICSA (Motif Identification for ChIP-Seq Analysis) combines this source of positional information with information on motif occurrences to better predict binding sites of transcription factors (TFs). We proved the greater accuracy of MICSA with respect to several other tools by running them on datasets for the TFs NRSF, GABP, STAT1 and CTCF. We also applied MICSA on a dataset for the oncogenic TF EWS-FLI1. We discovered >2000 binding sites and two functionally different binding motifs. We observed that EWS-FLI1 can activate gene transcription when (i) its binding site is located in close proximity to the gene transcription start site (up to ∼150 kb), and (ii) it contains a microsatellite sequence. Furthermore, we observed that sites without microsatellites can also induce regulation of gene expression—positively as often as negatively—and at much larger distances (up to ∼1 Mb)