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The pan-genome of a species is the union of the genes and non-coding sequences present in all individuals (cultivar, accessions, or strains) within that species.

Here we introduce PGV, a reference-agnostic representation of the pan-genome of a species based on the notion of consensus ordering. Our experimental results demonstrate that PGV enables an intuitive, effective and interactive visualization of a pan-genome by providing a genome browser that can elucidate complex structural genomic variations.

The PGV software can be installed via conda or downloaded fromhttps://github.com/ucrbioinfo/PGV. The companion PGV browser athttp://pgv.cs.ucr.educan be tested using example bed tracks available from the GitHub page.

High-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.

We present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.

https://github.com/ucrbioinfo/Selfish

A 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.

https://github.com/ucrbioinfo/Chimericognizer

Supplementary data are available at Bioinformatics online.

De novo genome assembly is a challenging computational problem due to the high repetitive content of eukaryotic genomes and the imperfections of sequencing technologies (i.e. sequencing errors, uneven sequencing coverage and chimeric reads). Several assembly tools are currently available, each of which has strengths and weaknesses in dealing with the trade-off between maximizing contiguity and minimizing assembly errors (e.g. mis-joins). To obtain the best possible assembly, it is common practice to generate multiple assemblies from several assemblers and/or parameter settings and try to identify the highest quality assembly. Unfortunately, often there is no assembly that both maximizes contiguity and minimizes assembly errors, so one has to compromise one for the other.

The concept of assembly reconciliation has been proposed as a way to obtain a higher quality assembly by merging or reconciling all the available assemblies. While several reconciliation methods have been introduced in the literature, we have shown in one of our recent papers that none of them can consistently produce assemblies that are better than the assemblies provided in input. Here we introduce Novo&Stitch, a novel method that takes advantage of optical maps to accurately carry out assembly reconciliation (assuming that the assembled contigs are sufficiently long to be reliably aligned to the optical maps, e.g. 50 Kbp or longer). Experimental results demonstrate that Novo&Stitch can double the contiguity (N50) of the input assemblies without introducing mis-joins or reducing genome completeness.

Novo&Stitch can be obtained from https://github.com/ucrbioinfo/Novo_Stitch.