Bioinformatics Challenges Research Articles

Proteomics and phosphoproteomics in precision medicine: applications and challenges.

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

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 Pacific Biosciences (PacBio) or even Oxford Nanopore Technologies (ONT) MinION reads.ResultsThe tools were tested on synthetic and real datasets from two technologies (PacBio and ONT MinION). Alignment quality and resource usage were compared across different aligners. 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. Our 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.Availability and implementation https://github.com/kkrizanovic/RNAseqEval, https://figshare.com/projects/RNAseq_benchmark/24391Supplementary information Supplementary data are available at Bioinformatics online.

MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes.

Advances in the sequencing of uncultured environmental samples, dubbed metagenomics, raise a growing need for accurate taxonomic assignment. Accurate identification of organisms present within a community is essential to understanding even the most elementary ecosystems. However, current high-throughput sequencing technologies generate short reads which partially cover full-length marker genes and this poses difficult bioinformatic challenges for taxonomy identification at high resolution. We designed MATAM, a software dedicated to the fast and accurate targeted assembly of short reads sequenced from a genomic marker of interest. The method implements a stepwise process based on construction and analysis of a read overlap graph. It is applied to the assembly of 16S rRNA markers and is validated on simulated, synthetic and genuine metagenomes. We show that MATAM outperforms other available methods in terms of low error rates and recovered fractions and is suitable to provide improved assemblies for precise taxonomic assignments. https://github.com/bonsai-team/matam. pierre.pericard@gmail.com or helene.touzet@univ-lille1.fr. Supplementary data are available at Bioinformatics online.

Unified Deep Learning Architecture for Modeling Biology Sequence.

Prediction of the spatial structure or function of biological macromolecules based on their sequences remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, long-range interaction, complicated and variable output of labeled structures, and variable length of biological sequences usually lead to different solutions on a case-by-case basis. This study proposed a unified deep learning architecture based on long short-term memory or a gated recurrent unit to capture long-range interactions. The architecture designs the optional reshape operator to adapt to the diversity of the output labels and implements a training algorithm to support the training of sequence models capable of processing variable-length sequences. The merging and pooling operators enhances the ability of capturing short-range interactions between basic units of biological sequences. The proposed deep-learning architecture and its training algorithm might be capable of solving currently variable biological sequence-modeling problems under a unified framework. We validated the model on one of the most difficult biological sequence-modeling problems, protein residue interaction prediction. The results indicate that the accuracy of obtaining the residue interactions of the model exceeded popular approaches by 10 percent on multiple widely-used benchmarks.

Current and Emerging Technologies for Probing Molecular Signatures of Traumatic Brain Injury.

Traumatic brain injury (TBI) is understood as an interplay between the initial injury, subsequent secondary injuries, and a complex host response all of which are highly heterogeneous. An understanding of the underlying biology suggests a number of windows where mechanistically inspired interventions could be targeted. Unfortunately, biologically plausible therapies have to-date failed to translate into clinical practice. While a number of stereotypical pathways are now understood to be involved, current clinical characterization is too crude for it to be possible to characterize the biological phenotype in a truly mechanistically meaningful way. In this review, we examine current and emerging technologies for fuller biochemical characterization by the simultaneous measurement of multiple, diverse biomarkers. We describe how clinically available techniques such as cerebral microdialysis can be leveraged to give mechanistic insights into TBI pathobiology and how multiplex proteomic and metabolomic techniques can give a more complete description of the underlying biology. We also describe spatially resolved label-free multiplex techniques capable of probing structural differences in chemical signatures. Finally, we touch on the bioinformatics challenges that result from the acquisition of such large amounts of chemical data in the search for a more mechanistically complete description of the TBI phenotype.

PCSF: An R-package for network-based interpretation of high-throughput data.

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.

Comparative analysis of targeted long read sequencing approaches for characterization of a plant\u2019s immune receptor repertoire

BackgroundThe Oxford Nanopore Technologies MinION™ sequencer is a small, portable, low cost device that is accessible to labs of all sizes and attractive for in-the-field sequencing experiments. Selective breeding of crops has led to a reduction in genetic diversity, and wild relatives are a key source of new genetic resistance to pathogens, usually via NLR immune receptor-encoding genes. Recent studies have demonstrated how crop NLR repertoires can be targeted for sequencing on Illumina or PacBio (RenSeq) and the specific gene conveying pathogen resistance identified.ResultsSequence yields per MinION run are lower than Illumina, making targeted resequencing an efficient approach. While MinION generates long reads similar to PacBio it doesn’t generate the highly accurate multipass consensus reads, which presents downstream bioinformatics challenges. Here we demonstrate how MinION data can be used for RenSeq achieving similar results to the PacBio and how novel NLR gene fusions can be identified via a Nanopore RenSeq pipeline.ConclusionThe described library preparation and bioinformatics methods should be applicable to other gene families or any targeted long DNA fragment nanopore sequencing project.

Abstract 487: A comprehensive target enrichment panel for fusion detection

Abstract Growing interest in cancer classification and progression has accelerated the rate of novel gene fusions discovery with increasing recognition of their roles as biomarkers. RNA-Seq is an attractive method for expressed fusion discovery and detection because of its ability to provide unbiased fusion sequencing information. The ability to detect low expressing fusion transcripts, however, require high sequencing depth and represents a significant financial barrier and identification of clinically relevant fusion sequences from a large data set can be a bioinformatics challenge. To address these challenges we have tested the Ovation® Fusion Panel Target Enrichment System V2, a targeted RNA sequencing method using the Single Primer Enrichment Technology (SPET), with a number of control and clinical samples. Initial studies were performed using a comprehensive target enrichment panel targeting 502 genes with three samples from Horizon DX containing known fusions. Target enriched libraries were constructed with 10 ng and 100 ng inputs and the data was analyzed using the NuFuseD pipeline (available as a point and click BaseSpace application or downloadable linux package) which has been optimized for fusion analysis from this data. Expected fusions were identified at both input levels, even when down sampled to 500K reads, with fewer fusion calls compared to other publically available fusion detection software (Chimerascan and SOAPFuse), suggesting a lower false positive rate. NuFuseD fusion calls are provided with a P-value to help prioritize the identified fusions for subsequent validation. Additionally, NuFuseD detected novel fusions in the control samples demonstrating the advantage of a comprehensive panel compared to more restricted panels. We further validated the target panel using control RNA (UHR and Human Brain) and fresh or FFPE cell lines (NCI-H2228, HCC1937) to further demonstrate our ability to identify known fusions. Finally, the system was evaluated at an external site using patient FFPE samples. These samples (N=8) were from a set of breast, liver and ovarian cancers, containing a unique fusion in 4 of the samples based on DNA based sequencing. Only 1 of the 4 expected fusions were identified using whole transcriptome data (100 million reads) while 3 of the 4 fusions were detected with this assay (10 million reads) demonstrating its ability to generate targeted RNA sequencing libraries with increased sensitivity of gene fusion detection and reduced sequencing costs compared to standard RNA-Seq methods. Citation Format: Ashesh Saraiya, Brandon Young, Tobias Meißner, Brian L. Jones, Stephanie C. Huelga, Doug A. Amorese. A comprehensive target enrichment panel for fusion detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 487. doi:10.1158/1538-7445.AM2017-487

Bioinformatics challenges in molecular epidemiology of cancers

Bioinformatics challenges in molecular epidemiology of cancers

MicroTaboo: a general and practical solution to the k-disjoint problem

BackgroundA common challenge in bioinformatics is to identify short sub-sequences that are unique in a set of genomes or reference sequences, which can efficiently be achieved by k-mer (k consecutive nucleotides) counting. However, there are several areas that would benefit from a more stringent definition of “unique”, requiring that these sub-sequences of length W differ by more than k mismatches (i.e. a Hamming distance greater than k) from any other sub-sequence, which we term the k-disjoint problem. Examples include finding sequences unique to a pathogen for probe-based infection diagnostics; reducing off-target hits for re-sequencing or genome editing; detecting sequence (e.g. phage or viral) insertions; and multiple substitution mutations. Since both sensitivity and specificity are critical, an exhaustive, yet efficient solution is desirable.ResultsWe present microTaboo, a method that allows for efficient and extensive sequence mining of unique (k-disjoint) sequences of up to 100 nucleotides in length. On a number of simulated and real data sets ranging from microbe- to mammalian-size genomes, we show that microTaboo is able to efficiently find all sub-sequences of a specified length W that do not occur within a threshold of k mismatches in any other sub-sequence. We exemplify that microTaboo has many practical applications, including point substitution detection, sequence insertion detection, padlock probe target search, and candidate CRISPR target mining.ConclusionsmicroTaboo implements a solution to the k-disjoint problem in an alignment- and assembly free manner. microTaboo is available for Windows, Mac OS X, and Linux, running Java 7 and higher, under the GNU GPLv3 license, at: https://MohammedAlJaff.github.io/microTaboo

Hybrid Wrapper/Filter Gene Selection Using an Ensemble of Classifiers and PSO Algorithm

Bioinformatics has grown very quickly for the last 20 years, and it will grow even faster in the future. One of the long-standing open challenges in bioinformatics is biomarker identification and cancer diagnosis from gene expression. In this paper, the authors propose a novel hybrid wrapper/filter feature selection approach to identify the most informative genes for cancer diagnosis, named HWF-GS. It handles selection through two steps. The first one is an iterative filter-based mechanism to generate potential subsets of genes. The second step is the aggregation of the best-selected subsets by means of a wrapper-based consensus process that relies on a particle swarm optimization adapted to feature selection. An ensemble of classifiers (SVM and KNN) is employed to evaluate the selected genes. Experiments on nine publicly available cancer DNA microarray datasets have shown that HWF-GS selects robust signatures with high classification accuracy and competes with and even outperforms other methods in the literature.

MetCCS predictor: a web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics.

In metabolomics, rigorous structural identification of metabolites presents a challenge for bioinformatics. The use of collision cross-section (CCS) values of metabolites derived from ion mobility-mass spectrometry effectively increases the confidence of metabolite identification, but this technique suffers from the limit number of available CCS values. Currently, there is no software available for rapidly generating the metabolites' CCS values. Here, we developed the first web server, namely, MetCCS Predictor, for predicting CCS values. It can predict the CCS values of metabolites using molecular descriptors within a few seconds. Common users with limited background on bioinformatics can benefit from this software and effectively improve the metabolite identification in metabolomics. The web server is freely available at: http://www.metabolomics-shanghai.org/MetCCS/ . jiangzhu@sioc.ac.cn. Supplementary data are available at Bioinformatics online.

Comparative network stratification analysis for identifying functional interpretable network biomarkers

BackgroundA major challenge of bioinformatics in the era of precision medicine is to identify the molecular biomarkers for complex diseases. It is a general expectation that these biomarkers or signatures have not only strong discrimination ability, but also readable interpretations in a biological sense. Generally, the conventional expression-based or network-based methods mainly capture differential genes or differential networks as biomarkers, however, such biomarkers only focus on phenotypic discrimination and usually have less biological or functional interpretation. Meanwhile, the conventional function-based methods could consider the biomarkers corresponding to certain biological functions or pathways, but ignore the differential information of genes, i.e., disregard the active degree of particular genes involved in particular functions, thereby resulting in less discriminative ability on phenotypes. Hence, it is strongly demanded to develop elaborate computational methods to directly identify functional network biomarkers with both discriminative power on disease states and readable interpretation on biological functions.ResultsIn this paper, we present a new computational framework based on an integer programming model, named as Comparative Network Stratification (CNS), to extract functional or interpretable network biomarkers, which are of strongly discriminative power on disease states and also readable interpretation on biological functions. In addition, CNS can not only recognize the pathogen biological functions disregarded by traditional Expression-based/Network-based methods, but also uncover the active network-structures underlying such dysregulated functions underestimated by traditional Function-based methods. To validate the effectiveness, we have compared CNS with five state-of-the-art methods, i.e. GSVA, Pathifier, stSVM, frSVM and AEP on four datasets of different complex diseases. The results show that CNS can enhance the discriminative power of network biomarkers, and further provide biologically interpretable information or disease pathogenic mechanism of these biomarkers. A case study on type 1 diabetes (T1D) demonstrates that CNS can identify many dysfunctional genes and networks previously disregarded by conventional approaches.ConclusionTherefore, CNS is actually a powerful bioinformatics tool, which can identify functional or interpretable network biomarkers with both discriminative power on disease states and readable interpretation on biological functions. CNS was implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/CNSpackage_0.1.rar.

Ant colony optimization based hierarchical multi-label classification algorithm

There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels, where a sample belongs to only a single class label. In these problems, known as flat classification problems, class labels are independent of each other. Unfortunately, on the other hand, less attention is given to the genre of classification problems where samples may belong to several classes and at the same time the class labels are organized based on a structured hierarchy; such as gene ontology, protein function prediction, test scores, web page categorization, text categorization etc. This article presents a novel Ant Colony Optimization based hierarchical multi-label classification algorithm that can handle such a complex instance of classification problems and can incorporates the given class hierarchy during its learning phase. The algorithm produces IF-THEN ordered rule list to learn a comprehensible model which can easily be verified by experts. It exploits positive correlation between the domain values of two related attributes to improve the discrimination power of resultant classification model, up to a significant level. The paper contains rich details regarding hierarchical single label (or single path) and multi-label classification problems and different categories of corresponding solutions. The proposed method is evaluated on sixteen most challenging bioinformatics datasets; some of these containing hundreds of attributes and thousands of class labels. At the end, the proposed method is compared with four recent state of the art hierarchical multi-label classification algorithms. The empirical evaluation confirms the promising ability of the proposed technique for hierarchical multi-label classification task.

FocusHeuristics \u2013 expression-data-driven network optimization and disease gene prediction

To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.

Critical Issues in Mycobiota Analysis.

Fungi constitute an important part of the human microbiota and they play a significant role for health and disease development. Advancements made in the culture-independent analysis of microbial communities have broadened our understanding of the mycobiota, however, microbiota analysis tools have been mainly developed for bacteria (e.g., targeting the 16S rRNA gene) and they often fall short if applied to fungal marker-gene based investigations (i.e., internal transcribed spacers, ITS). In the current paper we discuss all major steps of a fungal amplicon analysis starting with DNA extraction from specimens up to bioinformatics analyses of next-generation sequencing data. Specific points are discussed at each step and special emphasis is placed on the bioinformatics challenges emerging during operational taxonomic unit (OTU) picking, a critical step in mycobiota analysis. By using an in silico ITS1 mock community we demonstrate that standard analysis pipelines fall short if used with default settings showing erroneous fungal community representations. We highlight that switching OTU picking to a closed reference approach greatly enhances performance. Finally, recommendations are given on how to perform ITS based mycobiota analysis with the currently available measures.

An end-to-end software solution for the analysis of high-throughput single-cell migration data

The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory- and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their high-throughput data processing.

An Efficient Exact Method For Identifying Mutated Driver Pathways In Cancer

One of the key challenges in bioinformatics is to find the distinction between driver mutations that lead to tumorigenesis and random passenger mutations, which are known to be neutral and does not play any role in cancer development. To solve this problem, several approaches and techniques were used. In this article, we introduce an algorithm based on an exact approach in order to solve the so called “maximum weight submatrix problem”. The proposed algorithm maximizes the weight and samples all the possible pathways that can be found. We present the details about our algorithm and then compare them to a metaheuristic algorithm called Genetic Algorithm (GA) and with another exact model named Binary Linear Programming (BLP). Our Exact Algorithm shows good results in terms of maximizing the weight and finding all possible pathways.

10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science – BBC2017

We present the 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science – BBC2017, held in Zurich, 12 - 14 June 2017.

Combining independent de novo assemblies optimizes the coding transcriptome for nonconventional model eukaryotic organisms.

BackgroundNext-generation sequencing (NGS) technologies are arguably the most revolutionary technical development to join the list of tools available to molecular biologists since PCR. For researchers working with nonconventional model organisms one major problem with the currently dominant NGS platform (Illumina) stems from the obligatory fragmentation of nucleic acid material that occurs prior to sequencing during library preparation. This step creates a significant bioinformatic challenge for accurate de novo assembly of novel transcriptome data. This challenge becomes apparent when a variety of modern assembly tools (of which there is no shortage) are applied to the same raw NGS dataset. With the same assembly parameters these tools can generate markedly different assembly outputs.ResultsIn this study we present an approach that generates an optimized consensus de novo assembly of eukaryotic coding transcriptomes. This approach does not represent a new assembler, rather it combines the outputs of a variety of established assembly packages, and removes redundancy via a series of clustering steps. We test and validate our approach using Illumina datasets from six phylogenetically diverse eukaryotes (three metazoans, two plants and a yeast) and two simulated datasets derived from metazoan reference genome annotations. All of these datasets were assembled using three currently popular assembly packages (CLC, Trinity and IDBA-tran). In addition, we experimentally demonstrate that transcripts unique to one particular assembly package are likely to be bioinformatic artefacts. For all eight datasets our pipeline generates more concise transcriptomes that in fact possess more unique annotatable protein domains than any of the three individual assemblers we employed. Another measure of assembly completeness (using the purpose built BUSCO databases) also confirmed that our approach yields more information.ConclusionsOur approach yields coding transcriptome assemblies that are more likely to be closer to biological reality than any of the three individual assembly packages we investigated. This approach (freely available as a simple perl script) will be of use to researchers working with species for which there is little or no reference data against which the assembly of a transcriptome can be performed.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1406-x) contains supplementary material, which is available to authorized users.