Downstream Computational Analysis Research Articles

Unraveling the developmental heterogeneity within the human retina to reconstruct the continuity of retinal ganglion cell maturation and stage-specific intrinsic and extrinsic factors.

Tissue development is a complex spatiotemporal process with multiple interdependent components. Anatomical, histological, sequencing, and evolutional strategies can be used to profile and explain tissue development from different perspectives. The introduction of scRNAseq methods and the computational tools allows to deconvolute developmental heterogeneity and draw a decomposed uniform map. In this manuscript, we decomposed the development of a human retina with a focus on the retinal ganglion cells (RGC). To increase the temporal resolution of retinal cell classes maturation state we assumed the working hypothesis that that maturation of retinal ganglion cells is a continuous, non-discrete process. We have assembled the scRNAseq atlas of human fetal retina from fetal week 8 to week 27 and applied the computational methods to unravel maturation heterogeneity into a uniform maturation track. We align RGC transcriptomes in pseudotime to map RGC developmental fate trajectories against the broader timeline of retinal development. Through this analysis, we identified the continuous maturation track of RGC and described the cell-intrinsic (DEGs, maturation gene profiles, regulons, transcriptional motifs) and -extrinsic profiles (neurotrophic receptors across maturation, cell-cell interactions) of different RGC maturation states. We described the genes involved in the retina and RGC maturation, including de novo RGC maturation drivers. We demonstrate the application of the human fetal retina atlas as a reference tool, allowing automated annotation and universal embedding of scRNAseq data. Altogether, our findings deepen the current knowledge of the retina and RGC maturation by bringing in the maturation dimension for the cell class vs. state analysis. We show how the pseudotime application contributes to developmental-oriented analyses, allowing to order the cells by their maturation state. This approach not only improves the downstream computational analysis but also provides a true maturation track transcriptomics profile.

Inference and design of antibody specificity: From experiments to models and back.

Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.

Real-time, multiplexed rendering of lymphoma diagnostic panels from unstained tissue sections using virtual staining.

115 Background: Lymphoproliferative disorders are complex, heterogeneous diseases with an ever expanding array of subtypes and variants requiring stepwise assay approach to arrive at a precise diagnosis. While extensive characterization of lesional cells and the microenvironment (TME) via immunophenotyping and genotyping remains the gold standard, a full workup may be unachievable due to limited input tissue, turnaround time pressure, or unavailability of particular markers in-house. Even when available, the interpretation of large numbers of markers by pathologists is limited by the ability to mentally spatially align markers by recall-superimposition of single-plex markers, limiting the discovery of patterns relevant for novel treatments strategies. Here we present a deep-learning based virtual staining method which transforms a single unstained tissue section into a panel of perfectly aligned virtual immunohistochemical (IHC) stained images. Methods: 4um unstained sections were cut from 30 formalin-fixed paraffin-embedded blocks. Using a standard commercial slide scanner (Axio Scan.Z1, Zeiss), autofluorescence images were captured from unstained sections and paired with brightfield images captured from the same sections after chemical staining with H&E or various IHC stains of interest. The virtual staining was performed by a deep neural network trained as a conditional GAN using accurately co-registered patches of paired images . The training dataset comprised seven types of lymphoma plus benign lymphoid tissues. Results: We successfully created models for virtual CD3, CD8, CD20 and CD30 IHC stains as well as H&E. Analysis by board certified pathologists confirmed consistent and accurate results between virtual and IHC stained slides in a range of lymphomas and reactive lymphoid tissue. We quantitatively evaluated the co-expression of CD3/CD8 and the spatial distribution of all four markers in eight holdout slides. We demonstrated that our virtual multiplex IHC staining achieves comparable performance for WSI scoring and cellular localization with actual staining across lymphoma types. Conclusions: We present the feasibility of spatially aligned, rapid virtual H&E and IHC staining of four different markers from single, label-free autofluorescence images using deep-learning techniques. To the best of our knowledge, this is the first report of a panel of virtual IHC stains, perfectly aligned and with overlaid H&E structural context, for analysis of neoplastic and reactive lymphoid tissues. Our technique provides an opportunity for a rapid, comprehensive histopathology and immunophenotyping workflow. Multiplex virtual staining greatly expands diagnostic and discovery opportunities: high accuracy TME profiling, seamless downstream computational image analysis, and screening of large biomarker pools in retrospective analysis of clinical trials.

Heavy Methyl SILAC Metabolic Labeling of Human Cell Lines for High-Confidence Identification of R/K-Methylated Peptides by High-Resolution Mass Spectrometry.

Protein methylation is a widespread post-translational modification (PTM) involved in several important biological processes including, but not limited to, RNA splicing, signal transduction, translation, and DNA repair. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is considered today the most versatile and accurate technique to profile PTMs with high precision and proteome-wide depth; however, the identification of protein methylations by MS is still prone to high false discovery rates. In this chapter, we describe the heavy methyl SILAC metabolic labeling strategy that allows high-confidence identification of in vivo methyl-peptides by MS-based proteomics. We provide a general protocol that covers the steps of heavy methyl labeling of cultured cells, protein sample preparation, LC-MS/MS analysis, and downstream computational analysis of the acquired MS data.

129. Standardization of cancer terminology in the Mondo Disease Ontology

Biomedical ontologies provide a standard computable representation of knowledge that can be used to integrate and navigate large amounts of heterogeneous data for downstream computational analysis and knowledge discovery. The Mondo Disease Ontology (Mondo) is a semantic resource that integrates several existing disease terminologies, provides precise, curated semantic mappings between them, and unifies them into one coherent classification of diseases. Cancer concepts in Mondo are formally classified into a hierarchical representation, which can be used to annotate data at different levels of granularity. Neoplasm is the top-level class and subtypes (subclasses) are largely aligned with the NCIt neoplasm branch, with malignant neoplasms (cancers) being classified as subtypes. Classes are grouped in multiple ways, such as by anatomical entities affected (such as cardiovascular or digestive system neoplasms), by malignancy (benign, pre-malignant, or malignant, or onset (childhood neoplasm). In addition, Mondo has representations of susceptibilities to cancers and hereditary neoplastic syndromes such as Lynch Syndrome and Li-Fraumeni syndrome. Mondo provides mappings to other disease resources such as the National Cancer Institute Thesaurus (NCIt), as well as cancer terminology from Orphanet, OMIM, Disease Ontology, and others. Mondo precisely annotates each mapping using strict semantics, so that we know when two diseases are precisely equivalent or merely closely related. Mondo is iteratively developed and revised and we invite the community to contribute to Mondo; visit github.com/monarch-initiative/mondo for details.

BRIDE v2: A Validated Collection of Genes Involved in the Mammalian Brain Response to Low-Dose Ionizing Radiation

There is significant interest in the response of the mammalian brain to low-dose ionizing radiation (LDIR), mainly examined by gene or protein expression, with applications in radiation safety on Earth, the atmosphere and outer space. Potential associations of molecular-level responses with sensory or cognitive defects and neurodegenerative diseases are currently under investigation. Previously, we have described a light-weight approach for the storage, analysis and distribution of relevant datasets, with the platform BRIDE. We have re-implemented the platform as BRIDE v2 on the cloud, using the bioinformatics infrastructure ELIXIR. We connected the annotated list of 3174 unique gene records with modern omics resources for downstream computational analysis. BRIDE v2 is a cloud-based platform with capabilities that enable researchers to extract, analyze, visualize as well as export the gene collection. The resource is freely available online at <http://bride-db.eu>.

P1649: GFI1B AND LSD1 REPRESS A MYELOID DIFFERENTIATION PROGRAM DURING INDUCED PLURIPOTENT STEM CELL DERIVED MEGAKARYOCYTE DIFFERENTIATION

Background: Megakaryocytic development is driven by the transcription factor Growth Factor Independence 1B (GFI1B) and its interaction with the chromatin remodeler Lysine Specific Demethylase 1 (LSD1/KDM1A). GFI1B aberrations are linked to inherited bleeding disorders, resulting in macro-thrombocytopenia, gray platelets and increased expression of CD34. One such mutation, which was previously show to be the disease-causing mutation in a family with gray-platelet syndrome, introduces a premature stop codon (Q287*) in the fifth zinc finger. Aims: Here, we study the effect of the Q287* mutation on gene expression regulation, as well as GFI1B interaction with LSD1, during induced pluripotent stem cells (IPSC) derived megakaryocytic development. Methods: We differentiated IPSCs that contain wildtype GFI1B or GFI1BQ287* into megakaryocytes. During differentiation, megakaryocytes derived from GFI1B wildtype IPSCs were also treated with an LSD1 inhibitor that disrupts GFI1B-LSD1 interaction for 24 hours. The transcriptomic landscape of differentiated IPSCs was explored using single-cell RNA sequencing and downstream computational analysis. We applied a novel filtering strategy, removing cells based on doublet annotation, mitochondrial gene count and exclusive differential expression of ribosomal or mitochondrial genes. Results: After filtering and data integration we retained 14,848 cells. Within this population we were able to annotate six different cell types using single-cell GSEA and publicly available reference datasets. Megakaryocytes and megakaryocyte progenitors made up roughly 75% of all cells differentiated from IPSCs containing wildtype GFI1B. They are characterized by approximately 220 differentially expressed genes (p-value < 0.05, log-fold change > 0.69), including common megakaryocyte markers such as CD41 and CD42b. Strikingly, we also observed a population of myeloid and myeloid-progenitor cells after differentiation towards megakaryocytes. This population specifically expressed common myeloid markers, such as VIM, CD81, and SPI1 as well as approximately 200 other genes (p-value < 0.05, log-fold change > 0.69) and made up around 10% of the entire GFI1B wildtype cell population. This population was expanded two-fold in cells differentiated from GFI1B-wildtype IPSCs treated with the LSD1 inhibitor and three-fold in GFI1BQ287* IPSCs. Notably, in the GFI1BQ287* cell population megakaryocytes were reduced by 2-fold. Pseudotime analysis showed a differentiation trajectory starting at megakaryocytes and going towards myeloid cells. Megakaryocyte and platelet associated genes, such as CD41, CD42b and PF4, are gradually downregulated along this trajectory while myeloid associated genes exhibited a mirrored expression pattern. Thus, LSD1i treated and GFI1BQ287* IPSC-derived cells showed an increase in myeloid marker gene expression after differentiation and downregulation of coagulation-associated genes, hinting at a decreased potential to differentiate into megakaryocytes. Summary/Conclusion: Our data show that GFI1B and LSD1 function to repress a myeloid cell fate during IPSC-induced megakaryocytic development. Disruption of the interaction between GFI1B and LSD1 and a dominant-negative GFI1B mutation results in myeloid differentiation at the expense of megakaryocyte development.

TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets.

Although the ability to programmatically summarize and visually inspect sequencing data is an integral part of genome analysis, currently available methods are not capable of handling large numbers of samples. In particular, making a visual comparison of transcriptional landscapes between two sets of thousands of RNA-seq samples is limited by available computational resources, which can be overwhelmed due to the sheer size of the data. In this work, we present TieBrush, a software package designed to process very large sequencing datasets (RNA, whole-genome, exome, etc.) into a form that enables quick visual and computational inspection. TieBrush can also be used as a method for aggregating data for downstream computational analysis, and is compatible with most software tools that take aligned reads as input. TieBrush is provided as a C++ package under the MIT License. Precompiled binaries, source code and example data are available on GitHub (https://github.com/alevar/tiebrush). Supplementary data are available at Bioinformatics online.

EPID-20. NOVEL GLIOBLASTOMA POPULATION-BASED HISTOLOGIC STAIN NORMALIZATION

Abstract Histopathologic evaluation has been an integral part of clinical diagnosis for central nervous system tumors, providing information essential for classification, management, and treatment of the disease. Hematoxylin and eosin (H&amp;E) staining is routinely used in histology, providing detail of tissue morphology, structure, and cellular composition. MOTIVATION: Slide staining is rife with color intensity variations, mainly due to differences in materials and staining protocols among others. These variations introduce inaccuracies in downstream computational analysis and quantification of disease, disabling the generalization of computational models. To overcome these variations, current approaches arbitrarily select a slide within the cohort to normalize all slides of the cohort, leading to non-reproducible results in other cohorts. We develop a population-based whole slide image (WSI) normalization method based on overall region driven stain vectors and color histogram, weighted by corresponding percent contribution to overall slide (PCOS). METHODS: We identified a cohort of 509 H&amp;E stained WSIs with corresponding anatomical annotations from the Ivy Glioblastoma Atlas Project. These WSIs and annotations were reviewed by two neuropathologists for correctly annotated regions. Each region was weighted according to PCOS, WSIs with PCOS &amp;lt; 0.05% were discarded. Then, the optical densities and histograms calculated. Resulting color histogram and optical density was applied to the WSI cohort. Finally, stain intensity variability pre- and post- normalization was compared. RESULTS: Normalizing WSIs based on our approach, results in a significant (p &amp;lt; 0.01, Wilcoxon) improvement in color intensity variation for eight of nine regions tested, with the exception of “Pseudopalisading Cells with no visible Necrosis” (p = 0.8). DISCUSSION: This novel transformative technique is insensitive to artificially staining background density and straightforward to apply. Furthermore, the approach shows promise towards a viable and robust tool for stain normalization in large WSIs cohorts, with the potential towards a stain normalization standard generalizable to other diseases.

Intron retention and its impact on gene expression and protein diversity: A review and a practical guide.

Intron retention (IR) occurs when a complete and unspliced intron remains in mature mRNA. An increasing body of literature has demonstrated a major role for IR in numerous biological functions, including several that impact human health and disease. Although experimental technologies used to study other forms of mRNA splicing can also be used to investigate IR, a specialized downstream computational analysis is optimal for IR discovery and analysis. Here we provide a review of IR and its biological implications, as well as a practical guide for how to detect and analyze it. Several methods, including long read third generation direct RNA sequencing, are described. We have developed an R package, FakIR, to facilitate the execution of the bioinformatic tasks recommended in this review and a tutorial on how to fit them to users aims. Additionally, we provide guidelines and experimental protocols to validate IR discovery and to evaluate the potential impact of IR on gene expression and protein output. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Splicing Regulation/Alternative Splicing RNA Methods > RNA Analyses in vitro and In Silico.

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling.

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This publication presents a complete interaction proteomics workflow designed for identifying low abundance protein-protein interactions from the nucleus that could also be applied to other subcellular compartments. This workflow includes subcellular fractionation, immunoprecipitation, sample preparation, offline cleanup, single-shot label-free mass spectrometry, and downstream computational analysis and data visualization. Our protocol is optimized for detecting compartmentalized, low abundance interactions that are difficult to identify from whole cell lysates (e.g., transcription factor interactions in the nucleus) by immunoprecipitation of endogenous proteins from fractionated subcellular compartments. The sample preparation pipeline outlined here provides detailed instructions for the preparation of HeLa cell nuclear extract, immunoaffinity purification of endogenous bait protein, and quantitative mass spectrometry analysis. We also discuss methodological considerations for performing large-scale immunoprecipitation in mass spectrometry-based interaction profiling experiments and provide guidelines for evaluating data quality to distinguish true positive protein interactions from nonspecific interactions. This approach is demonstrated here by investigating the nuclear interactome of the CMGC kinase, DYRK1A, a low abundance protein kinase with poorly defined interactions within the nucleus.

An RNA-Seq Protocol for Differential Expression Analysis.

Here we consider RNA-Seq, used to measure global gene expression through RNA fragmentation, capture, sequencing, and subsequent computational analysis. Xenopus, with its large number of RNA-rich, synchronously developing, and accessible embryos, is an excellent model organism for exploiting the power of high-throughput sequencing to understand gene expression during development. Here we present a standard RNA-Seq protocol for performing two-state differential gene expression analysis (between groups of replicates of control and treated embryos) using Illumina sequencing. Samples contain multiple whole embryos, and polyadenylated mRNA is measured under relative normalization. The protocol is divided into two parts: wet-lab processes to prepare samples for sequencing and downstream computational analysis including quality control, quantification of gene expression, and differential expression.

Super-Resolution Digital Pathology Image Processing of Bone Marrow Aspirate and Cytology Smears and Tissue Sections

Background: Accurate digital pathology image analysis depends on high-quality images. As such, it is imperative to obtain digital images with high resolution for downstream data analysis. While hematoxylin and eosin (H&E)-stained tissue section slides from solid tumors contain three-dimensional information, these data have been ignored in digital pathology. In addition, in cytology and bone marrow aspirate smears, the three-dimensional nature of the specimen has precluded efficient analysis of such morphologic data. An individual image snapshot at a single focal distance is often not sufficient for accurate diagnoses and multiple whole-slide images at different focal distances are necessary for diagnostics. Materials and Methods: We describe a novel computational pipeline and processing program for obtaining a super-resolved image from multiple static images at different z-planes in overlapping but separate frames. This program, MULTI-Z, performs image alignment, Gaussian smoothing, and Laplacian filtering to construct a final super-resolution image from multiple images. Results: We applied this algorithm and program to images of cytology and H&E-stained sections and demonstrated significant improvements in both resolution and image quality by objective data analyses (24% increase in sharpness and focus). Conclusions: With the use of our program, super-resolved images of cytology and H&E-stained tissue sections can be obtained to potentially allow for more optimal downstream computational analysis. This method is applicable to whole-slide scanned images.

A microchip platform for structural oncology applications.

Recent advances in the development of functional materials offer new tools to dissect human health and disease mechanisms. The use of tunable surfaces is especially appealing as substrates can be tailored to fit applications involving specific cell types or tissues. Here we use tunable materials to facilitate the three-dimensional (3D) analysis of BRCA1 gene regulatory complexes derived from human cancer cells. We employed a recently developed microchip platform to isolate BRCA1 protein assemblies natively formed in breast cancer cells with and without BRCA1 mutations. The captured assemblies proved amenable to cryo-electron microscopy (EM) imaging and downstream computational analysis. Resulting 3D structures reveal the manner in which wild-type BRCA1 engages the RNA polymerase II (RNAP II) core complex that contained K63-linked ubiquitin moieties—a putative signal for DNA repair. Importantly, we also determined that molecular assemblies harboring the BRCA15382insC mutation exhibited altered protein interactions and ubiquitination patterns compared to wild-type complexes. Overall, our analyses proved optimal for developing new structural oncology applications involving patient-derived cancer cells, while expanding our knowledge of BRCA1’s role in gene regulatory events.

SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands

Uncovering the relationships between peptide and protein sequences and binding properties is critical for successfully predicting, re-designing and inhibiting protein–protein interactions. Systematically collected data that link protein sequence to binding are valuable for elucidating determinants of protein interaction but are rare in the literature because such data are experimentally difficult to generate. Here we describe SORTCERY, a high-throughput method that we have used to rank hundreds of yeast-displayed peptides according to their affinities for a target interaction partner. The procedure involves fluorescence-activated cell sorting of a library, deep sequencing of sorted pools and downstream computational analysis. We have developed theoretical models and statistical tools that assist in planning these stages. We demonstrate SORTCERY's utility by ranking 1026 BH3 (Bcl-2 homology 3) peptides with respect to their affinities for the anti-apoptotic protein Bcl-xL. Our results are in striking agreement with measured affinities for 19 individual peptides with dissociation constants ranging from 0.1 to 60nM. High-resolution ranking can be used to improve our understanding of sequence–function relationships and to support the development of computational models for predicting and designing novel interactions.

GlyXalign: High‐throughput migration time alignment preprocessing of electrophoretic data retrieved via multiplexed capillary gel electrophoresis with laser‐induced fluorescence detection‐based glycoprofiling

Glycomics has become a rapidly emerging field and monitoring of protein glycosylation is needed to ensure quality and consistency during production processes of biologicals such as therapeutic antibodies or vaccines. Glycoanalysis via multiplexed CGE with LIF detection (xCGE-LIF) represents a powerful technique featuring high resolution, high sensitivity as well as high-throughput performance. However, sample data retrieved from this method exhibit challenges for downstream computational analysis due to intersample migration time shifts as well as stretching and compression of electropherograms. Here, we present glyXalign, a freely available and easy-to-use software package to automatically correct for distortions in xCGE-LIF based glycan data. We demonstrate its ability to outperform conventional algorithms such as dynamic time warping and correlation optimized warping in terms of processing time and alignment accuracy for high-resolution datasets. Built upon a set of rapid algorithms, the tool includes an intuitive graphical user interface and allows full control over all parameters. Additionally, it visualizes the alignment process and enables the user to readjust misaligned results. Software and documentation are available at http://www.glyxera.com.

FROM BINDING MOTIFS IN CHIP-SEQ DATA TO IMPROVED MODELS OF TRANSCRIPTION FACTOR BINDING SITES

Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) became a method of choice to locate DNA segments bound by different regulatory proteins. ChIP-Seq produces extremely valuable information to study transcriptional regulation. The wet-lab workflow is often supported by downstream computational analysis including construction of models of nucleotide sequences of transcription factor binding sites in DNA, which can be used to detect binding sites in ChIP-Seq data at a single base pair resolution. The most popular TFBS model is represented by positional weight matrix (PWM) with statistically independent positional weights of nucleotides in different columns; such PWMs are constructed from a gapless multiple local alignment of sequences containing experimentally identified TFBSs. Modern high-throughput techniques, including ChIP-Seq, provide enough data for careful training of advanced models containing more parameters than PWM. Yet, many suggested multiparametric models often provide only incremental improvement of TFBS recognition quality comparing to traditional PWMs trained on ChIP-Seq data. We present a novel computational tool, diChIPMunk, that constructs TFBS models as optimal dinucleotide PWMs, thus accounting for correlations between nucleotides neighboring in input sequences. diChIPMunk utilizes many advantages of ChIPMunk, its ancestor algorithm, accounting for ChIP-Seq base coverage profiles ("peak shape") and using the effective subsampling-based core procedure which allows processing of large datasets. We demonstrate that diPWMs constructed by diChIPMunk outperform traditional PWMs constructed by ChIPMunk from the same ChIP-Seq data. Software website: http://autosome.ru/dichipmunk/

Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data

BackgroundHigh-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular the systems integration required to fully exploit and compare data from different studies and/or technology platforms. In transcriptomics, the identification of differentially expressed genes when studying effect(s) or contrast(s) of interest constitutes the starting point for further downstream computational analysis (e.g. gene over-representation/enrichment analysis, reverse engineering) leading to mechanistic insights. Therefore, it is important to systematically store the full list of genes with their associated statistical analysis results (differential expression, t-statistics, p-value) corresponding to one or more effect(s) or contrast(s) of interest (shortly termed as ” contrast data”) in a comparable manner and extract gene sets in order to efficiently support downstream analyses and further leverage data on a long-term basis. Filling this gap would open new research perspectives for biologists to discover disease-related biomarkers and to support the understanding of molecular mechanisms underlying specific biological perturbation effects (e.g. disease, genetic, environmental, etc.).ResultsTo address these challenges, we developed Confero, a contrast data and gene set platform for downstream analysis and biological interpretation of omics data. The Confero software platform provides storage of contrast data in a simple and standard format, data transformation to enable cross-study and platform data comparison, and automatic extraction and storage of gene sets to build new a priori knowledge which is leveraged by integrated and extensible downstream computational analysis tools. Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA) are currently integrated as an analysis module as well as additional tools to support biological interpretation. Confero is a standalone system that also integrates with Galaxy, an open-source workflow management and data integration system. To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset.ConclusionConfero provides a unique and flexible platform to support downstream computational analysis facilitating biological interpretation. The system has been designed in order to provide the researcher with a simple, innovative, and extensible solution to store and exploit analyzed data in a sustainable and reproducible manner thereby accelerating knowledge-driven research. Confero source code is freely available from http://sourceforge.net/projects/confero/.

The HDAC interaction network

The HDAC interaction network

Taxonomic binning of metagenome samples generated by next-generation sequencing technologies

Metagenome research uses random shotgun sequencing of microbial community DNA to study the genetic sequences of its members without cultivation. This development has been strongly supported by improvements in sequencing technologies, which have rendered sequencing cheaper than before. As a consequence, downstream computational analysis of metagenome sequence samples is now faced with large amounts of complex data. One of the essential steps in metagenome analysis is reconstruction of draft genomes for populations of a community or of draft 'pan-genomes' for higher level clades. 'Taxonomic binning' corresponds to the process of assigning a taxonomic identifier to sequence fragments, based on information such as sequence similarity, sequence composition or read coverage. This is used for draft genome reconstruction, if sequencing coverage is insufficient for reconstruction based on assembly information alone. Subsequent functional and metabolic annotation of draft genomes allows a genome-level analysis of novel uncultured microbial species and even inference of their cultivation requirements.