MOTIVATION: Genome-wide association studies (GWASs) have identified thousands of common trait-associated genetic variants but interpretation of their function remains challenging. These genetic variants can overlap the binding sites of transcription factors (TFs) and therefore could alter gene expression. However, we currently lack a systematic understanding on how this mechanism contributes to phenotype. RESULTS: We present Motif-Raptor, a TF-centric computational tool that integrates sequence-based predictive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants. Given trait-associated non-coding variants, Motif-Raptor can recover relevant cell types and critical TFs to drive hypotheses regarding their mechanism of action. We tested Motif-Raptor on complex traits such as rheumatoid arthritis and red blood cell count and demonstrated its ability to prioritize relevant cell types, potential regulatory TFs and non-coding SNPs which have been previously characterized and validated. AVAILABILITY AND IMPLEMENTATION: Motif-Raptor is freely available as a Python package at: https://github.com/pinellolab/MotifRaptor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics. Results: Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three diverse scoring functions from MyriMatch, Comet and the original Sipros were incorporated within a single database searching engine. Supervised classification with logistic regression was used to filter database searching results. Benchmarking with soil and marine microbial communities demonstrated a higher number of peptide and protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator, MS-GF+/Percolator, Comet & MyriMatch/iProphet and Comet & MyriMatch & MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on supercomputers. Availability and implementation: Freely available under the GNU GPL license at http://sipros.omicsbio.org. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

SoyMetDB is a metabolomic database for soybean, developed to target the growing needs of the soybean community. The goal is to provide a one-stop web resource for integrating, mining and visualizing soybean metabolomic data, including identification and expression of various metabolites across different experiments and time courses. It incorporates GC-MS and LC-MS based metabolite-profiling data dynamically linked to metabolite information from other public metabolomic databases, including HMDB and Knapsack. SoyMetDB includes Arabidopsis metabolomic data for cross-species comparisons and can retrieve information including the expression patterns of various experiments for complete or partial metabolite name queries. It also incorporates a pathway viewer tool integrating the data from various experimental conditions and presenting them on the pathways to highlight the expressed metabolite, and identifies the most highly represented pathways for multiple metabolite queries. SoyMetDB can be accessed at http://soymetdb.org.

Lossless compression of encrypted sources can be achieved through Slepian-Wolf coding. For encrypted real-world sources, such as images, the key to improve the compression efficiency is how the source dependency is exploited. Approaches in the literature that make use of Markov properties in the Slepian-Wolf decoder do not work well for grayscale images. In this correspondence, we propose a resolution progressive compression scheme which compresses an encrypted image progressively in resolution, such that the decoder can observe a low-resolution version of the image, study local statistics based on it, and use the statistics to decode the next resolution level. Good performance is observed both theoretically and experimentally.

Compression of encrypted data can be viewed as a special case of distributed source coding and can be achieved by applying Slepian-Wolf coding. However, how to compress the encrypted video efficiently remains a challenging problem especially for those videos with irregular high motion. This paper proposes a novel multi-resolution based approach which makes it possible not only to effectively derive the temporal side information from previous frames, but also to generate the spatial side information by having partial access to the current frame. The spatial and temporal side information can then be integrated adaptively to facilitate the compression. Simulation results show that the proposed scheme significantly outperforms other schemes, especially for those video clips with irregular high motion.

Compression of encrypted data can be achieved by employing Slepian-Wolf coding (SWC). However, how to efficiently exploit the source dependency in an encrypted colored signal such as an image remains a challenging issue. Previous works incorporate 2-D Markov models in the SWC, which is not accurate enough for natural grayscale images; as a result, the compression performance is usually poor. In this paper, we propose to compress the image progressively, such that the decoder can observe a low-resolution version of the image, from which local statistics is learned and used for the decoding of the next resolution level. Good performance is observed both theoretically and experimentally.