Bioinformatics

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Identifying simultaneous rearrangements in cancer genomes

Mon, 2017-11-27 02:00
Abstract
Motivation
The traditional view of cancer evolution states that a cancer genome accumulates a sequential ordering of mutations over a long period of time. However, in recent years it has been suggested that a cancer genome may instead undergo a one-time catastrophic event, such as chromothripsis, where a large number of mutations instead occur simultaneously. A number of potential signatures of chromothripsis have been proposed. In this work, we provide a rigorous formulation and analysis of the ‘ability to walk the derivative chromosome’ signature originally proposed by Korbel and Campbell. In particular, we show that this signature, as originally envisioned, may not always be present in a chromothripsis genome and we provide a precise quantification of under what circumstances it would be present. We also propose a variation on this signature, the H/T alternating fraction, which allows us to overcome some of the limitations of the original signature.
Results
We apply our measure to both simulated data and a previously analyzed real cancer dataset and find that the H/T alternating fraction may provide useful signal for distinguishing genomes having acquired mutations simultaneously from those acquired in a sequential fashion.
Availability and implementation
An implementation of the H/T alternating fraction is available at https://bitbucket.org/oesperlab/ht-altfrac.
Contact
loesper@carleton.edu
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

QVZ: lossy compression of quality values

Tue, 2017-11-21 02:00
Bioinformatics (2015) 31(19), 3122–3129
Categories: Bioinformatics, Journals

Identifying structural variants using linked-read sequencing data

Fri, 2017-11-03 02:00
Abstract
Motivation
Structural variation, including large deletions, duplications, inversions, translocations and other rearrangements, is common in human and cancer genomes. A number of methods have been developed to identify structural variants from Illumina short-read sequencing data. However, reliable identification of structural variants remains challenging because many variants have breakpoints in repetitive regions of the genome and thus are difficult to identify with short reads. The recently developed linked-read sequencing technology from 10X Genomics combines a novel barcoding strategy with Illumina sequencing. This technology labels all reads that originate from a small number (∼5 to 10) DNA molecules ∼50 Kbp in length with the same molecular barcode. These barcoded reads contain long-range sequence information that is advantageous for identification of structural variants.
Results
We present Novel Adjacency Identification with Barcoded Reads (NAIBR), an algorithm to identify structural variants in linked-read sequencing data. NAIBR predicts novel adjacencies in an individual genome resulting from structural variants using a probabilistic model that combines multiple signals in barcoded reads. We show that NAIBR outperforms several existing methods for structural variant identification—including two recent methods that also analyze linked-reads—on simulated sequencing data and 10X whole-genome sequencing data from the NA12878 human genome and the HCC1954 breast cancer cell line. Several of the novel somatic structural variants identified in HCC1954 overlap known cancer genes.
Availability and implementation
Software is available at compbio.cs.brown.edu/software.
Contact
braphael@princeton.edu
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

DISC: DISulfide linkage Characterization from tandem mass spectra

Mon, 2017-10-23 02:00
Abstract
Motivation
Enzymatic digestion under appropriate reducing conditions followed by mass spectrometry analysis has emerged as the primary method for disulfide bond analysis. The large amount of mass spectral data collected in the mass spectrometry experiment requires effective computational approaches to automate the interpretation process. Although different approaches have been developed for such purpose, they always choose to ignore the frequently observed internal ion fragments and they lack a reasonable quality control strategy and calibrated scoring scheme for the statistical validation and ranking of the reported results.
Results
In this research, we present a new computational approach, DISC (DISulfide bond Characterization), for matching an input MS/MS spectrum against the putative disulfide linkage structures hypothetically constructed from the protein database. More specifically, we consider different ion types including a variety of internal ions that frequently observed in mass spectra resulted from disulfide linked peptides, and introduce an effective two-layer scoring scheme to evaluate the significance of the matching between spectrum and structure, based on which we have also developed a useful target-decoy strategy for providing quality control and reporting false discovery rate in the final results. Systematic experiments conducted on both low-complexity and high-complexity datasets demonstrated the efficiency of our proposed method for the identification of disulfide bonds from MS/MS spectra, and showed its potential in characterizing disulfide bonds at the proteome scale instead of just a single protein.
Availability and implementation
Software is available for downloading at http://www.csd.uwo.ca/yliu766/.
Contact
yliu766@uwo.ca
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

Identification of copy number variations and translocations in cancer cells from Hi-C data

Wed, 2017-10-18 02:00
Abstract
Motivation
Eukaryotic chromosomes adapt a complex and highly dynamic three-dimensional (3D) structure, which profoundly affects different cellular functions and outcomes including changes in epigenetic landscape and in gene expression. Making the scenario even more complex, cancer cells harbor chromosomal abnormalities [e.g. copy number variations (CNVs) and translocations] altering their genomes both at the sequence level and at the level of 3D organization. High-throughput chromosome conformation capture techniques (e.g. Hi-C), which are originally developed for decoding the 3D structure of the chromatin, provide a great opportunity to simultaneously identify the locations of genomic rearrangements and to investigate the 3D genome organization in cancer cells. Even though Hi-C data has been used for validating known rearrangements, computational methods that can distinguish rearrangement signals from the inherent biases of Hi-C data and from the actual 3D conformation of chromatin, and can precisely detect rearrangement locations de novo have been missing.
Results
In this work, we characterize how intra and inter-chromosomal Hi-C contacts are distributed for normal and rearranged chromosomes to devise a new set of algorithms (i) to identify genomic segments that correspond to CNV regions such as amplifications and deletions (HiCnv), (ii) to call inter-chromosomal translocations and their boundaries (HiCtrans) from Hi-C experiments and (iii) to simulate Hi-C data from genomes with desired rearrangements and abnormalities (AveSim) in order to select optimal parameters for and to benchmark the accuracy of our methods. Our results on 10 different cancer cell lines with Hi-C data show that we identify a total number of 105 amplifications and 45 deletions together with 90 translocations, whereas we identify virtually no such events for two karyotypically normal cell lines. Our CNV predictions correlate very well with whole genome sequencing data among chromosomes with CNV events for a breast cancer cell line (r = 0.89) and capture most of the CNVs we simulate using Avesim. For HiCtrans predictions, we report evidence from the literature for 30 out of 90 translocations for eight of our cancer cell lines. Furthermore, we show that our tools identify and correctly classify relatively understudied rearrangements such as double minutes and homogeneously staining regions. Considering the inherent limitations of existing techniques for karyotyping (i.e. missing balanced rearrangements and those near repetitive regions), the accurate identification of CNVs and translocations in a cost-effective and high-throughput setting is still a challenge. Our results show that the set of tools we develop effectively utilize moderately sequenced Hi-C libraries (100–300 million reads) to identify known and de novo chromosomal rearrangements/abnormalities in well-established cancer cell lines. With the decrease in required number of cells and the increase in attainable resolution, we believe that our framework will pave the way towards comprehensive mapping of genomic rearrangements in primary cells from cancer patients using Hi-C.
Availability and implementation
CNV calling: https://github.com/ay-lab/HiCnv, Translocation calling: https://github.com/ay-lab/HiCtrans and Hi-C simulation: https://github.com/ay-lab/AveSim.
Contact
ferhatay@lji.org
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

CCmiR: a computational approach for competitive and cooperative microRNA binding prediction

Mon, 2017-09-25 02:00
Abstract
Motivation
The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites.
Results
Tested on four different datasets, CCmiR predicted miRNA target sites with a high recall and a reasonable precision, and identified known and new cooperative and competitive miRNAs supported by literature. Compared with three state-of-the-art computational methods, CCmiR had a higher recall and a higher precision.
Availability and implementation
CCmiR is freely available at http://hulab.ucf.edu/research/projects/miRNA/CCmiR.
Contact
xiaoman@mail.ucf.edu or haihu@cs.ucf.edu
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

Phandango: an interactive viewer for bacterial population genomics

Mon, 2017-09-25 02:00
Abstract
Summary
Fully exploiting the wealth of data in current bacterial population genomics datasets requires synthesizing and integrating different types of analysis across millions of base pairs in hundreds or thousands of isolates. Current approaches often use static representations of phylogenetic, epidemiological, statistical and evolutionary analysis results that are difficult to relate to one another. Phandango is an interactive application running in a web browser allowing fast exploration of large-scale population genomics datasets combining the output from multiple genomic analysis methods in an intuitive and interactive manner.
Availability and implementation
Phandango is a web application freely available for use at www.phandango.net and includes a diverse collection of datasets as examples. Source code together with a detailed wiki page is available on GitHub at https://github.com/jameshadfield/phandango.
Contact
jh22@sanger.ac.uk or sh16@sanger.ac.uk
Categories: Bioinformatics, Journals

In silico structural modeling of multiple epigenetic marks on DNA

Mon, 2017-09-25 02:00
Abstract
There are four known epigenetic cytosine modifications in mammals: methylation (5mC), hydroxymethylation (5hmC), formylation (5fC) and carboxylation (5caC). The biological effects of 5mC are well understood but the roles of the remaining modifications remain elusive. Experimental and computational studies suggest that a single epigenetic mark has little structural effect but six of them can radically change the structure of DNA to a new form, F-DNA. Investigating the collective effect of multiple epigenetic marks requires the ability to interrogate all possible combinations of epigenetic states (e.g. methylated/non-methylated) along a stretch of DNA. Experiments on such complex systems are only feasible on small, isolated examples and there currently exist no systematic computational solutions to this problem. We address this issue by extending the use of Natural Move Monte Carlo to simulate the conformations of epigenetic marks. We validate our protocol by reproducing in silico experimental observations from two recently published high-resolution crystal structures that contain epigenetic marks 5hmC and 5fC. We further demonstrate that our protocol correctly finds either the F-DNA or the B-DNA states more energetically favorable depending on the configuration of the epigenetic marks. We hope that the computational efficiency and ease of use of this novel simulation framework would form the basis for future protocols and facilitate our ability to rapidly interrogate diverse epigenetic systems.
Availability and implementation
The code together with examples and tutorials are available from http://www.cs.ox.ac.uk/mosaics
Contact
peter.minary@cs.ox.ac.uk
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

RINspector: a Cytoscape app for centrality analyses and DynaMine flexibility prediction

Fri, 2017-09-22 02:00
Abstract
Motivation
Protein function is directly related to amino acid residue composition and the dynamics of these residues. Centrality analyses based on residue interaction networks permit to identify key residues in a protein that are important for its fold or function. Such central residues and their environment constitute suitable targets for mutagenesis experiments. Predicted flexibility and changes in flexibility upon mutation provide valuable additional information for the design of such experiments.
Results
We combined centrality analyses with DynaMine flexibility predictions in a Cytoscape app called RINspector. The app performs centrality analyses and directly visualizes the results on a graph of predicted residue flexibility. In addition, the effect of mutations on local flexibility can be calculated.
Availability and implementation
The app is publicly available in the Cytoscape app store.
Contact
guillaume.brysbaert@univ-lille1.fr
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

TROVE: a user-friendly tool for visualizing and analyzing cancer hallmarks in signaling networks

Fri, 2017-09-22 02:00
Abstract
Summary
Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, an user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types.
Availability and implementation
Available under the Eclipse Public License from: https://sites.google.com/site/cosbyntu/softwares/trove and https://github.com/trove2017/Trove.
Contact
hechua@ntu.edu.sg or assourav@ntu.edu.sg
Categories: Bioinformatics, Journals

HoTResDB: host transcriptional response database for viral hemorrhagic fevers

Fri, 2017-09-22 02:00
Abstract
Summary
High-throughput screening of the host transcriptional response to various viral infections provides a wealth of data, but utilization of microarray and next generation sequencing (NGS) data for analysis can be difficult. The Host Transcriptional Response DataBase (HoTResDB), allows visitors to access already processed microarray and NGS data from non-human primate models of viral hemorrhagic fever to better understand the host transcriptional response.
Availability
HoTResDB is freely available at http://hotresdb.bu.edu
Contact
jhconnor@bu.edu
Categories: Bioinformatics, Journals

Detecting presence of mutational signatures in cancer with confidence

Fri, 2017-09-22 02:00
Abstract
Motivation
Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. The decomposition of a cancer genome’s mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to be able to not only decompose a patient’s mutational profile into signatures but also establish the accuracy of such decomposition.
Results
We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified both very stable and highly unstable signatures, as well as signatures that previously have not been associated with breast cancer. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions.
Availability and implementation
All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi\#signatureestimation.
Contact
wojtowda@ncbi.nlm.nih.gov or przytyck@ncbi.nlm.nih.gov
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

IntPred: a structure-based predictor of protein–protein interaction sites

Mon, 2017-09-18 02:00
Abstract
Motivation
Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods.
Results
On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent.
Availability and implementation
IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/.
Contact
andrew@bioinf.org.uk or andrew.martin@ucl.ac.uk
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

A novel SCCA approach via truncated ℓ1-norm and truncated group lasso for brain imaging genetics

Mon, 2017-09-18 02:00
Abstract
Motivation
Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ1-norm or its variants to induce sparsity. The ℓ0-norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem.
Results
In this paper, we propose the truncated ℓ1-norm penalized SCCA to improve the performance and effectiveness of the ℓ1-norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning τ. It can avoid the time intensive parameter tuning if given a reasonable small τ. Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting imaging genetic associations.
Availability and implementation
The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/tlpscca/.
Contact
dulei@nwpu.edu.cn or shenli@iu.edu
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

Meta-server for automatic analysis, scoring and ranking of docking models

Mon, 2017-09-18 02:00
Abstract
Motivation
Modelling with multiple servers that use different algorithms for docking results in more reliable predictions of interaction sites. However, the scoring and comparison of all models by an expert is time-consuming and is not feasible for large volumes of data generated by such modelling.
Results
Quality ASsessment of DOcking Models (QASDOM) Server is a simple and efficient tool for real-time simultaneous analysis, scoring and ranking of data sets of receptor–ligand complexes built by a range of docking techniques. This meta-server is designed to analyse large data sets of docking models and rank them by scoring criteria developed in this study. It produces two types of output showing the likelihood of specific residues and clusters of residues to be involved in receptor–ligand interactions and the ranking of models. The server also allows visualizing residues that form interaction sites in the receptor and ligand sequence and displays 3D model structures of the receptor–ligand complexes.
Availability
http://qasdom.eimb.ru.
Contact
alexei.adzhubei@eimb.ru.
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

Mobi 2.0: an improved method to define intrinsic disorder, mobility and linear binding regions in protein structures

Mon, 2017-09-18 02:00
Abstract
Motivation
The structures contained in the Protein Data Bank (PDB) database are of paramount importance to define our knowledge of folded proteins. While providing mainly circumstantial evidence, PDB data is also increasingly used to define the lack of unique structure, represented by mobile regions and even intrinsic disorder (ID). However, alternative definitions are used by different authors and potentially limit the generality of the analyses being carried out.
Results
Here we present Mobi 2.0, a completely re-written version of the Mobi software for the determination of mobile and potentially disordered regions from PDB structures. Mobi 2.0 provides robust definitions of mobility based on four main sources of information: (i) missing residues, (ii) residues with high temperature factors, (iii) mobility between different models of the same structure and (iv) binding to another protein or nucleotide chain. Mobi 2.0 is well suited to aggregate information across different PDB structures for the same UniProt protein sequence, providing consensus annotations. The software is expected to standardize the treatment of mobility, allowing an easier comparison across different studies related to ID.
Availability
Mobi 2.0 provides the structure-based annotation for the MobiDB database. The software is available from URL http://protein.bio.unipd.it/mobi2/.
Contact
silvio.tosatto@unipd.it
Categories: Bioinformatics, Journals

MemBrain-contact 2.0: a new two-stage machine learning model for the prediction enhancement of transmembrane protein residue contacts in the full chain

Fri, 2017-09-15 02:00
Abstract
Motivation
Inter-residue contacts in proteins have been widely acknowledged to be valuable for protein 3 D structure prediction. Accurate prediction of long-range transmembrane inter-helix residue contacts can significantly improve the quality of simulated membrane protein models.
Results
In this paper, we present an updated MemBrain predictor, which aims to predict transmembrane protein residue contacts. Our new model benefits from an efficient learning algorithm that can mine latent structural features, which exist in original feature space. The new MemBrain is a two-stage inter-helix contact predictor. The first stage takes sequence-based features as inputs and outputs coarse contact probabilities for each residue pair, which will be further fed into convolutional neural network together with predictions from three direct-coupling analysis approaches in the second stage. Experimental results on the training dataset show that our method achieves an average accuracy of 81.6% for the top L/5 predictions using a strict sequence-based jackknife cross-validation. Evaluated on the test dataset, MemBrain can achieve 79.4% prediction accuracy. Moreover, for the top L/5 predicted long-range loop contacts, the prediction performance can reach an accuracy of 56.4%. These results demonstrate that the new MemBrain is promising for transmembrane protein’s contact map prediction.
Availability and implementation
http://www.csbio.sjtu.edu.cn/bioinf/MemBrain/
Contact
hbshen@sjtu.edu.cn
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals

MetExploreViz: web component for interactive metabolic network visualization

Fri, 2017-09-15 02:00
Abstract
Summary
MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context.
Availability and implementation
Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/
Contact
contact-metexplore@inra.fr
Categories: Bioinformatics, Journals

SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

Thu, 2017-09-14 02:00
Abstract
Motivation
Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.
Results
We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development.
Availability and implementation
MATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites.
Contact
rudi.gunawan@chem.ethz.ch
Supplementary information
Supplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics, Journals