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Abstract SummaryA chimeric contig is contig that has been incorrectly assembled, i.e. a contig that contains one or more mis-joins. The detection of chimeric contigs can be carried out either by aligning assembled contigs to genome-wide maps (e.g. genetic, physical or optical maps) or by mapping sequenced reads to the assembled contigs. Here, we introduce a software tool called Chimericognizer that takes advantage of one or more Bionano Genomics optical maps to accurately detect and correct chimeric contigs. Experimental results show that Chimericognizer is very accurate, and significantly better than the chimeric detection method offered by the Bionano Hybrid Scaffold pipeline. Chimericognizer can also detect and correct chimeric optical molecules. Availability and implementationhttps://github.com/ucrbioinfo/Chimericognizer Supplementary informationSupplementary data are available at Bioinformatics online. 

Abstract MotivationHuman immunodeficiency virus type 1 (HIV-1) genome integration is closely related to clinical latency and viral rebound. In addition to human DNA sequences that directly interact with the integration machinery, the selection of HIV integration sites has also been shown to depend on the heterogeneous genomic context around a large region, which greatly hinders the prediction and mechanistic studies of HIV integration. ResultsWe have developed an attention-based deep learning framework, named DeepHINT, to simultaneously provide accurate prediction of HIV integration sites and mechanistic explanations of the detected sites. Extensive tests on a high-density HIV integration site dataset showed that DeepHINT can outperform conventional modeling strategies by automatically learning the genomic context of HIV integration from primary DNA sequence alone or together with epigenetic information. Systematic analyses on diverse known factors of HIV integration further validated the biological relevance of the prediction results. More importantly, in-depth analyses of the attention values output by DeepHINT revealed intriguing mechanistic implications in the selection of HIV integration sites, including potential roles of several DNA-binding proteins. These results established DeepHINT as an effective and explainable deep learning framework for the prediction and mechanistic study of HIV integration. Availability and implementationDeepHINT is available as an open-source software and can be downloaded from https://github.com/nonnerdling/DeepHINT. Supplementary informationSupplementary data are available at Bioinformatics online. 

Abstract MotivationAcross biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. ResultsHere, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users’ existing HPC pipelines. Availability and implementationData utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. Supplementary informationSupplementary data are available at Bioinformatics online. 

Abstract MotivationThe availability of numerous ChIP-seq datasets for transcription factors (TF) has provided an unprecedented opportunity to identify all TF binding sites in genomes. However, the progress has been hindered by the lack of a highly efficient and accurate tool to find not only the target motifs, but also cooperative motifs in very big datasets. ResultsWe herein present an ultrafast and accurate motif-finding algorithm, ProSampler, based on a novel numeration method and Gibbs sampler. ProSampler runs orders of magnitude faster than the fastest existing tools while often more accurately identifying motifs of both the target TFs and cooperators. Thus, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes. Availability and implementationSource code and binaries are freely available for download at https://github.com/zhengchangsulab/prosampler. It was implemented in C++ and supported on Linux, macOS and MS Windows platforms. Supplementary informationSupplementary materials are available at Bioinformatics online.