Hi-C is a genome-wide sequencing technique to investigate the 3D chromatin conformation inside the nucleus. The most studied structures that can be identified from Hi-C - chromatin interactions and topologically associating domains (TADs) - require computational methods to analyze genome-wide contact probability maps. We quantitatively compared the performances of 13 algorithms for the analysis of Hi-C data from 6 landmark studies and simulations. The comparison revealed clear differences in the performances of methods to identify chromatin interactions and more comparable results of algorithms for TAD detection.

The advent of high-throughput genome scale technologies has enabled us to unravel a large amount of the previously unknown transcriptionally active regions of the genome. Recent genome-wide studies have provided annotations of a large repertoire of various classes of noncoding transcripts. Long noncoding RNAs (lncRNAs) form a major proportion of these novel annotated noncoding transcripts, and presently known to be involved in a number of functionally distinct biological processes. Over 18 000 transcripts are presently annotated as lncRNA, and encompass previously annotated classes of noncoding transcripts including large intergenic noncoding RNA, antisense RNA and processed pseudogenes. There is a significant gap in the resources providing a stable annotation, cross-referencing and biologically relevant information. lncRNome has been envisioned with the aim of filling this gap by integrating annotations on a wide variety of biologically significant information into a comprehensive knowledgebase. To the best of our knowledge, lncRNome is one of the largest and most comprehensive resources for lncRNAs.Database URL: http://genome.igib.res.in/lncRNome