Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

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Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

New evidence and consensus has led to further revision of the McDonald Criteria for diagnosis of multiple sclerosis. The use of imaging for demonstration of dissemination of central nervous system lesions in space and time has been simplified, and in some circumstances dissemination in space and time can be established by a single scan. These revisions simplify the Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use.

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Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria

https://www.bioinf.uni-leipzig.de/Leere/WS1516/SeqAnalGen/T-Coffee.pdf

T-Coffee: A novel method for fast and accurate multiple sequence alignment.

A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era

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Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Ribonucleic acid (RNA) is a polymer composed of four bases denoted A, C, G, and U It generally is a single-stranded molecule where the bases form hydrogen bonds within the same molecule leading to structure formation In comparing different homologous RNA molecules it is important to consider both the base sequence and the structure of the molecules Traditional alignment algorithms can only account for the sequence of bases, but not for the base pairings Considering the structure leads to significant computational problems because of the dependencies introduced by the base pairings In this paper we address the problem of optimally aligning a given RNA sequence of unknown structure to one of known sequence and structure We phrase the problem as an integer linear program and then solve it using methods from polyhedral combinatorics In our computational experiments we could solve large problem instances--23S ribosomal RNA with more than 1400 bases--a size intractable for former algorithms

A polyhedral approach to RNA sequence structure alignment

We have investigated algorithms that are particularly suited for the parallel MD simulations of synthetic polymers. These algorithms distribute the atoms of the polymer among the processors. Dynamic nonbonded interactions, which are the difficult part of an MD simulation, are realised with the help of a special coarse-grained representation of the chain structure. We have devised and compared a master version and a distributed version of the algorithm. Surprisingly, the master version is competitive for a relatively large number of processors. We also investigated methods to improve load balancing. The resulting simulation package will be made available in the near future.

Simulating synthetic polymer chains in parallel

Large-scale antibody profiling of human blood sera: The future

Einleitung Biologische Daten sind heterogen, sehr komplex und oft sehr gros. Eine angemessene Visualisierung leistet hierbei einen entscheidenden Beitrag zum Verstandnis der Daten. Die Visualisierung biologischer Daten spielt daher auch eine zentrale Rolle in der Bioinformatik. Hier reichen die Anwendungen von der Visualisierung einzelner Proteine oder ganzer Genome, von Familien vonGenen, evolutionaren Verwandtschaftsverhaltnissen, makromolekularer Strukturen, mikroskopischer Bilddaten bis hin zur Darstellung von metabolischen oder regulatorischen Netzwerken und systembiologischer Daten (siehe Abb. 1 fur einige Beispiele). Aufgrund der zunehmenden Komplexitat und Verbundenheit biologischer Daten (man denke hier insbesondere an systembiologische Daten) ist eine integrative sowie standardisierte Visualisierung und die Entwicklung leistungsstarker und benutzerfreundlicher Werkzeuge von wachsender Bedeutung. Eine zunehmend grose Rolle spielen dabei Werkzeuge, die zudem das Paradigma der visuellen Analytik (engl. ,,visual analytics“) verfolgen. Grundsatzlich integriert die visuelle Analytik Visualisierung, Datenanalyse und Interaktion durch den Menschen. Bei biologischen Daten hat der Einsatz der visuellen Analytik daruber hinaus das Ziel, die komplexen experimentellen Daten in Wissen umzusetzen. Forschern soll ein System zur Verfugung gestellt werden, das es erlaubt, Einsichten in biologische Prozesse in Zellen, Geweben und schlieslich Organismen zu gewinnen sowie eine Modellierung biologischer Systeme vorzunehmen. Die visuelle Analytik biologischer Daten hat erst vor kurzem auf den Information-Visualizationund Visual-Analytics-Konferenzen Beachtung gefunden. Einige auch in wissenschaftlichen Zeitschriften publizierte Artikel sind Themen wie Clustering von Expressionsdaten [17], der GenomAssemblierung [18] oder der Bestimmung von Funktionen von Genen in neu sequenzierten Genomen [20] gewidmet. Die diesjahrige IEEE VAST Challenge stand ganz im Zeichen einer biologischen Fragestellung. Im folgenden Artikel mochten wir insbesondere auf drei der oben genannten Teilgebiete der Visualisierung biologischer Daten genauer eingehen: die visuelle Analytik von Genexpressionsdaten, die Visualisierung biologischer Netzwerke sowie die inhaltsbasierte Suche in zellbiologischen Bilddatenbanken.

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Visuelle Analytik biologischer Daten

Let P be a set of n points in the Euclidean plane and let C be a convex figure. In 1985, Chazelle and Edelsbrunner presented an algorithm, which preprocesses P such that for any query point q, the points of P in the translate C+q can be retrieved efficiently. Assuming that constant time suffices for deciding the inclusion of a point in C, they provided a space and query time optimal solution. Their algorithm uses O(n) space. A query with output size k can be solved in O(log n+k) time. The preprocessing step of their algorithm, however, has time complexity O(n2). We show that the usage of a new construction method for layers reduces the preprocessing time to O(n log n). We thus provide the first space, query time and preprocessing time optimal solution for this class of point retrieval problems. Besides, we present two new dynamic data structures for these problems. The first dynamic data structure allows on-line insertions and deletions of points in O((log n)2) time. In this dynamic data structure, a query with output size k can be solved in O(log n+k(log n)2) time. The second dynamic data structure, which allows only semi-online updates, has O((log n)2) amortized update time and O(log n+k) query time.

/pdf/an-optimal-construction-method-for-generalized-convex-layers-3cefs4gtt3.pdf

Hans-Peter Lenhof

Papers

A polyhedral approach to RNA sequence structure alignment

Simulating synthetic polymer chains in parallel

Large-scale antibody profiling of human blood sera: The future

Visuelle Analytik biologischer Daten

An optimal construction method for generalized convex layers