Hi-C is a common technique for assessing 3D chromatin conformation. Recent studies have shown that long-range interaction information in Hi-C data can be used to generate chromosome-length genome assemblies and identify large-scale structural variations. Here, we demonstrate the use of Hi-C data in detecting mobile transposable element (TE) insertions genome-wide. Our pipeline Hi-C-based TE analyzer (HiTea) capitalizes on clipped Hi-C reads and is aided by a high proportion of discordant read pairs in Hi-C data to detect insertions of three major families of active human TEs. Despite the uneven genome coverage in Hi-C data, HiTea is competitive with the existing callers based on whole-genome sequencing (WGS) data and can supplement the WGS-based characterization of the TE-insertion landscape. We employ the pipeline to identify TE-insertions from human cell-line Hi-C samples. AVAILABILITY AND IMPLEMENTATION: HiTea is available at https://github.com/parklab/HiTea and as a Docker image. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Despite the improvement in variant detection algorithms, visual inspection of the read-level data remains an essential step for accurate identification of variants in genome analysis. We developed BamSnap, an efficient BAM file viewer utilizing a graphics library and BAM indexing. In contrast to existing viewers, BamSnap can generate high-quality snapshots rapidly, with customized tracks and layout. As an example, we produced read-level images at 1000 genomic loci for >2500 whole-genomes. AVAILABILITY AND IMPLEMENTATION: BamSnap is freely available at https://github.com/parklab/bamsnap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate calling of structural variations such as deletions with short sequence reads from high-throughput sequencing is an important but challenging problem in the field of genome analysis. There are many existing methods for calling deletions. At present, not a single method clearly outperforms all other methods in precision and sensitivity. A popular strategy used by several authors is combining different signatures left by deletions in order to achieve more accurate deletion calling. However, most existing methods using the combining approach are heuristic and the called deletions by these tools still contain many wrongly called deletions. In this paper, we present Concod, a machine learning based framework for calling deletions with consensus, which is able to more accurately detect and distinguish true deletions from falsely called ones. First, Concod collects candidate deletions by merging the output of multiple existing deletion calling tools. Then, features of each candidate are extracted from aligned reads based on multiple detection theories. Finally, a machine learning model is trained with these features and used to classify the true and false candidates. We test our approach on different coverage of real data and compare with existing tools, including Pindel, SVseq2, BreakDancer, and DELLY. Results show that Concod improves both precision and sensitivity of deletion calling significantly.

BACKGROUND: Next-generation RNA sequencing technologies have been widely applied in transcriptome profiling. This facilitates further studies of gene structure and expression on the genome wide scale. It is an important step to align reads to the reference genome and call out splicing junctions for the following analysis, such as the analysis of alternative splicing and isoform construction. However, because of the existence of introns, when RNA-seq reads are aligned to the reference genome, reads can not be fully mapped at splicing sites. Thus, it is challenging to align reads and call out splicing junctions accurately. RESULTS: In this paper, we present a classification based approach for calling splicing junctions from RNA-seq data, which is implemented in the program SpliceJumper. SpliceJumper uses a machine learning approach which combines multiple features extracted from RNA-seq data. We compare SpliceJumper with two existing RNA-seq analysis approaches, TopHat2 and MapSplice2, on both simulated and real data. Our results show that SpliceJumper outperforms TopHat2 and MapSplice2 in accuracy. The program SpliceJumper can be downloaded at https://github.com/Reedwarbler/SpliceJumper.