HnRNPK maintains single strand RNA through controlling double-strand RNA in mammalian cells.

Although antisense transcription is a widespread event in the mammalian genome, double-stranded RNA (dsRNA) formation between sense and antisense transcripts is very rare and mechanisms that control dsRNA remain unknown. By characterizing the FGF-2 regulated transcriptome in normal and cancer cells, we identified sense and antisense transcripts IER3 and IER3-AS1 that play a critical role in FGF-2 controlled oncogenic pathways. We show that IER3 and IER3-AS1 regulate each other's transcription through HnRNPK-mediated post-transcriptional regulation. HnRNPK controls the mRNA stability and colocalization of IER3 and IER3-AS1. HnRNPK interaction with IER3 and IER3-AS1 determines their oncogenic functions by maintaining them in a single-stranded form. hnRNPK depletion neutralizes their oncogenic functions through promoting dsRNA formation and cytoplasmic accumulation. Intriguingly, hnRNPK loss-of-function and gain-of-function experiments reveal its role in maintaining global single- and double-stranded RNA. Thus, our data unveil the critical role of HnRNPK in maintaining single-stranded RNAs and their physiological functions by blocking RNA-RNA interactions.

Evaluation and assessment of read-mapping by multiple next-generation sequencing aligners based on genome-wide characteristics.

Massive data produced due to the advent of next-generation sequencing (NGS) technology is widely used for biological researches and medical diagnosis. The crucial step in NGS analysis is read alignment or mapping which is computationally intensive and complex. The mapping bias tends to affect the downstream analysis, including detection of polymorphisms. In order to provide guidelines to the biologist for suitable selection of aligners; we have evaluated and benchmarked 5 different aligners (BWA, Bowtie2, NovoAlign, Smalt and Stampy) and their mapping bias based on characteristics of 5 microbial genomes. Two million simulated read pairs of various sizes (36bp, 50bp, 72bp, 100bp, 125bp, 150bp, 200bp, 250bp and 300bp) were aligned. Specific alignment features such as sensitivity of mapping, percentage of properly paired reads, alignment time and effect of tandem repeats on incorrectly mapped reads were evaluated. BWA showed faster alignment followed by Bowtie2 and Smalt. NovoAlign and Stampy were comparatively slower. Most of the aligners showed high sensitivity towards long reads (>100bp) mapping. On the other hand NovoAlign showed higher sensitivity towards both short reads (36bp, 50bp, 72bp) and long reads (>100bp) mappings; It also showed higher sensitivity towards mapping a complex genome like Plasmodium falciparum. The percentage of properly paired reads aligned by NovoAlign, BWA and Stampy were markedly higher. None of the aligners outperforms the others in the benchmark, however the aligners perform differently with genome characteristics. We expect that the results from this study will be useful for the end user to choose aligner, thus enhance the accuracy of read mapping.

BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data.

Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the increasing creation of information. The BioMet Toolbox 2.0 integrates a number of functionalities in a user-friendly environment enabling the user to work with biological data in a web interface. The unique and distinguishing feature of the BioMet Toolbox 2.0 is to provide a web user interface to tools for metabolic pathways and omics analysis developed under different platform-dependent environments enabling easy access to these computational tools.