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Summary Management of raw-sequencing data and its pre-processing (conversion into sequences and demultiplexing) remains a challenging topic for groups running sequencing devices. They face many challenges in such efforts and solutions ranging from manual management of spreadsheets to very complex and customized laboratory information management systems handling much more than just sequencing raw data. In this article, we describe the software package DigestiFlow that focuses on the management of Illumina flow cell sample sheets and raw data. It allows for automated extraction of information from flow cell data and management of sample sheets. Furthermore, it allows for the automated and reproducible conversion of Illumina base calls to sequences and the demultiplexing thereof using bcl2fastq and Picard Tools, followed by quality control report generation. Availability and implementation The software is available under the MIT license at https://github.com/bihealth/digestiflow-server. The client software components are available via Bioconda. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: A series of methods in population genetics use multilocus genotype data to assign individuals membership in latent clusters. These methods belong to a broad class of mixedmembership models, such as latent Dirichlet allocation used to analyze text corpora. Inference from mixed-membership models can produce different output matrices when repeatedly applied to the same inputs, and the number of latent clusters is a parameter that is often varied in the analysis pipeline. For these reasons, quantifying, visualizing, and annotating the output from mixedmembership models are bottlenecks for investigators across multiple disciplines from ecology to text data mining. Results: We introduce pong, a network-graphical approach for analyzing and visualizing membership in latent clusters with a native interactive D3.js visualization. pong leverages efficient algorithms for solving the Assignment Problem to dramatically reduce runtime while increasing accuracy compared with other methods that process output from mixed-membership models. We apply pong to 225 705 unlinked genome-wide single-nucleotide variants from 2426 unrelated individuals in the 1000 Genomes Project, and identify previously overlooked aspects of global human population structure. We show that pong outpaces current solutions by more than an order of magnitude in runtime while providing a customizable and interactive visualization of population structure that is more accurate than those produced by current tools. [ABSTRACT FROM AUTHOR]

Motivation The number of genomes (including meta-genomes) is increasing at an accelerating pace. In the near future, we may need to estimate pairwise distances between millions of genomes. Even with the use of cloud computing, very few softwares can perform such estimation. Results The multi-threaded software BinDash can perform such estimation using only a typical personal laptop. BinDash implemented b -bit one-permutation rolling MinHash with optimal densification, an existing data-mining technique. BinDash empirically outperforms the state-of-the-art software in terms of precision, compression ratio, memory usage and runtime according to our evaluation. Our evaluation is performed with a Dell Inspiron 157 559 Notebook on all bacterial genomes in RefSeq. Availability and implementation BinDash is released under the Apache 2.0 license at https://github.com/zhaoxiaofei/BinDash. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Recent completion of the global proteomic characterization of The Cancer Genome Atlas (TCGA) colorectal cancer (CRC) cohort resulted in the first tumor dataset with complete molecular measurements at DNA, RNA and protein levels. Using CRC as a paradigm, we describe the application of the NetGestalt framework to provide easy access and interpretation of multi-omics data. Results: The NetGestalt CRC portal includes genomic, epigenomic, transcriptomic, proteomic and clinical data for the TCGA CRC cohort, data from other CRC tumor cohorts and cell lines, and existing knowledge on pathways and networks, giving a total of more than 17 million data points. The portal provides features for data query, upload, visualization and integration. These features can be flexibly combined to serve various needs of the users, maximizing the synergy among omics data, human visualization and quantitative analysis. Using three case studies, we demonstrate that the portal not only provides user-friendly data query and visualization but also enables efficient data integration within a single omics data type, across multiple omics data types, and over biological networks. [ABSTRACT FROM AUTHOR]

Summary: KODAMA, a novel learning algorithm for unsupervised feature extraction, is specifically designed for analysing noisy and high-dimensional datasets. Here we present an R package of the algorithm with additional functions that allow improved interpretation of high-dimensional data. The package requires no additional software and runs on all major platforms. [ABSTRACT FROM AUTHOR]

Motivation: The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chemically identifying metabolites from mass spectrometric signals present in complex datasets.Results: Three workflows have been developed to allow for the rapid, automated and high-throughput annotation and putative metabolite identification of electrospray LC-MS-derived metabolomic datasets. The collection of workflows are defined as PUTMEDID_LCMS and perform feature annotation, matching of accurate m/z to the accurate mass of neutral molecules and associated molecular formula and matching of the molecular formulae to a reference file of metabolites. The software is independent of the instrument and data pre-processing applied. The number of false positives is reduced by eliminating the inaccurate matching of many artifact, isotope, multiply charged and complex adduct peaks through complex interrogation of experimental data.Availability: The workflows, standard operating procedure and further information are publicly available at http://www.mcisb.org/resources/putmedid.html.Contact: warwick.dunn@manchester.ac.uk [ABSTRACT FROM AUTHOR]

Motivation: The exponential growth of scientific literature has resulted in a massive amount of unstructured natural language data that cannot be directly handled by means of bioinformatics tools. Such tools generally require structured data, often generated through a cumbersome process of manual literature curation. Herein, we present MyMiner, a free and user-friendly text annotation tool aimed to assist in carrying out the main biocuration tasks and to provide labelled data for the development of text mining systems. MyMiner allows easy classification and labelling of textual data according to user-specified classes as well as predefined biological entities. The usefulness and efficiency of this application have been tested for a range of real-life annotation scenarios of various research topics.Availability: http://myminer.armi.monash.edu.au.Contacts: david.salgado@monash.edu and christophe.marcelle@monash.eduSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: The Automated Mass Spectral Deconvolution and Identification System (AMDIS) is freeware extensively applied in metabolomics. However, datasets processed by AMDIS require extensive data correction, filtering and reshaping to create reliable datasets for further downstream analysis. Performed manually, these processes are laborious and extremely time consuming. Furthermore, manual corrections increase the chance of human error and can introduce additional technical variability to the data. Thus, an automated pipeline for curating GC-MS data is urgently needed.Results: We present the Metab R package designed to automate the pipeline for analysis of metabolomics GC-MS datasets processed by AMDIS.Availability: The Metab package, the AMDIS library and the reference ion library are available at www.metabolomics.auckland.ac.nz/index.php/downloads.Contact: k.ruggiero@auckland.ac.nz [ABSTRACT FROM PUBLISHER]

Motivation: Kinetic measurements have played an important role in elucidating biochemical and biophysical phenomena for over a century. While many tools for analysing kinetic measurements exist, most require low noise levels in the data, leaving outlier measurements to be cleaned manually. This is particularly true for protein misfolding and aggregation processes, which are extremely noisy and hence difficult to model. Understanding these processes is paramount, as they are associated with diverse physiological processes and disorders, most notably neurodegenerative diseases. Therefore, a better tool for analysing and cleaning protein aggregation traces is required. Results: Here we introduce Kfits, an intuitive graphical tool for detecting and removing noise caused by outliers in protein aggregation kinetics data. Following its workflow allows the user to quickly and easily clean large quantities of data and receive kinetic parameters for assessment of the results. With minor adjustments, the software can be applied to any type of kinetic measurements, not restricted to protein aggregation. [ABSTRACT FROM AUTHOR]

Motivation: There are many tools for variant calling and effect prediction, but little to tie together large sample groups. Aggregating, sorting and summarizing variants and effects across a cohort is often done with ad hoc scripts that must be re-written for every new project. In response, we have written MuCor, a tool to gather variants from a variety of input formats (including multiple files per sample), perform database lookups and frequency calculations, and write many types of reports. In addition to use in large studies with numerous samples, MuCor can also be employed to directly compare variant calls from the same sample across two or more platforms, parameters or pipelines. A companion utility, DepthGauge, measures coverage at regions of interest to increase confidence in calls. [ABSTRACT FROM AUTHOR]

Summary: Rapid identification of non-human sequences (RINS) is an intersection-based pathogen detection workflow that utilizes a user-provided custom reference genome set for identification of non-human sequences in deep sequencing datasets. In <2 h, RINS correctly identified the known virus in the dataset SRR73726 and is compatible with any computer capable of running the prerequisite alignment and assembly programs. RINS accurately identifies sequencing reads from intact or mutated non-human genomes in a dataset and robustly generates contigs with these non-human sequences (Supplementary Material).Availability: RINS is available for free download at http://khavarilab.stanford.edu/resources.htmlContact: abhaduri@stanford.edu or kqu@stanford.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: We present Proteomatic, an operating system independent and user-friendly platform that enables the construction and execution of MS/MS data evaluation pipelines using free and commercial software. Required external programs such as for peptide identification are downloaded automatically in the case of free software. Due to a strict separation of functionality and presentation, and support for multiple scripting languages, new processing steps can be added easily.Availability and Implementation: Proteomatic is implemented in C++/Qt, scripts are implemented in Ruby, Python and PHP. All source code is released under the LGPL. Source code and installers for Windows, Mac OS X, and Linux are freely available at http://www.proteomatic.org.Contact: michael.specht@uni-muenster.deSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: Due to the increasing number of text mining resources (tools and corpora) available to biologists, interoperability issues between these resources are becoming significant obstacles to using them effectively. UIMA, the Unstructured Information Management Architecture, is an open framework designed to aid in the construction of more interoperable tools. U-Compare is built on top of the UIMA framework, and provides both a concrete framework for out-of-the-box text mining and a sophisticated evaluation platform allowing users to run specific tools on any target text, generating both detailed statistics and instance-based visualizations of outputs. U-Compare is a joint project, providing the worlds largest, and still growing, collection of UIMA-compatible resources. These resources, originally developed by different groups for a variety of domains, include many famous tools and corpora. U-Compare can be launched straight from the web, without needing to be manually installed. All U-Compare components are provided ready-to-use and can be combined easily via a drag-and-drop interface without any programming. External UIMA components can also simply be mixed with U-Compare components, without distinguishing between locally and remotely deployed resources. Availability: http://u-compare.org/ Contact: kano@is.s.u-tokyo.ac.jp [ABSTRACT FROM AUTHOR]

Motivation: Most experimental evidence on kinetic parameters is buried in the literature, whose manual searching is complex, time consuming and partial. These shortcomings become particularly acute in systems biology, where these parameters need to be integrated into detailed, genome-scale, metabolic models. These problems are addressed by KiPar, a dedicated information retrieval system designed to facilitate access to the literature relevant for kinetic modelling of a given metabolic pathway in yeast. Searching for kinetic data in the context of an individual pathway offers modularity as a way of tackling the complexity of developing a full metabolic model. It is also suitable for large-scale mining, since multiple reactions and their kinetic parameters can be specified in a single search request, rather than one reaction at a time, which is unsuitable given the size of genome-scale models. Results: We developed an integrative approach, combining public data and software resources for the rapid development of large-scale text mining tools targeting complex biological information. The user supplies input in the form of identifiers used in relevant data resources to refer to the concepts of interest, e.g. EC numbers, GO and SBO identifiers. By doing so, the user is freed from providing any other knowledge or terminology concerned with these concepts and their relations, since they are retrieved from these and cross-referenced resources automatically. The terminology acquired is used to index the literature by mapping concepts to their synonyms, and then to textual documents mentioning them. The indexing results and the previously acquired knowledge about relations between concepts are used to formulate complex search queries aiming at documents relevant to the users information needs. The conceptual approach is demonstrated in the implementation of KiPar. Evaluation reveals that KiPar performs better than a Boolean search. The precision achieved for abstracts (60%) and full-text articles (48%) is considerably better than the baseline precision (44% and 24%, respectively). The baseline recall is improved by 36% for abstracts and by 100% for full text. It appears that full-text articles are a much richer source of information on kinetic data than are their abstracts. Finally, the combined results for abstracts and full text compared with the curated literature provide high values for relative recall (88%) and novelty ratio (92%), suggesting that the system is able to retrieve a high proportion of new documents. Availability: Source code and documentation are available at: http://www.mcisb.org/resources/kipar/ Contact: i.spasic@manchester.ac.uk; dbk@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: The Functional Genomics Experiment Object Model (FuGE) supports modelling of experimental processes either directly or through extensions that specialize FuGE for use in specific contexts. FuGE applications commonly include components that capture, store and search experiment descriptions, where the requirements of different applications have much in common. Results: We describe a toolkit that supports data capture, storage and web-based search of FuGE experiment models; the toolkit can be used directly on FuGE compliant models or configured for use with FuGE extensions. The toolkit is illustrated using a FuGE extension standardized by the proteomics standards initiative, namely GelML. Availability: The toolkit and a demonstration are available at http://code.google.com/p/fugetoolkit Contact: khalid.belhajjame@manchester.ac.uk [ABSTRACT FROM AUTHOR]