Biology subjects

Pal, S.

Publications and source records attributed to Pal, S..

3 recordsLinked to original sources

bioRxiv2018

Novel analysis of HT-SELEX data elucidates the role of DNA shape in transcription factor binding

Understanding the principles of DNA binding by transcription factors (TFs) is of primary importance for studying gene regulation. Recently, several lines of evidence suggested that both DNA sequence and shape contribute to TF binding. However, the question if in the absence of any sequence similarity to the binding motif, DNA shape can still increase probability of binding was yet to be addressed.\n\nTo address this challenge, we developed Co-SELECT, a computational approach to analyze the results of in vitro HT-SELEX experiments for TF-DNA binding. Specifically, the presence of motif-free sequences in late HT-SELEX rounds and their enrichment in weak binders allowed us to detect evidence for the role of DNA shape features in TF binding.\n\nOur approach revealed that, even in the absence of the sequence motif, TFs have propensity to weakly bind to DNA molecules enriched in specific shape features. Surprisingly, we also found that some properties of DNA shape contribute to promiscuous binding of all tested TF families. Strikingly, such promiscuously bound shapes correspond to the most frequent shape formed by the DNA. We propose that this promiscuous binding facilitates diffusing of TFs along the DNA molecule before it is locked in its binding site.

bioinformatics↗

bioRxiv2017

A Regression-based Framework for Scalable Pathway-guided Search in Genome-wide Association Studies.

Traditional unbiased genome-wide association studies (GWAS) have successfully identified thousands of loci associated with various complex diseases but there is evidence to suggest that many variants were missed at stringent genome-wide thresholds. Fortunately, there is a rapidly increasing amount of prior knowledge in publicly available genomic datasets and biological databases that can be harnessed to enhance the power of discovering SNPs/Genes from existing or new GWAS datasets. For most diseases, many of the identified loci tend to cluster into a few specific biological pathways/networks. From the point of view of disease etiology, such clustering is generally to be expected. This phenomenon can be exploited to conduct a more powerful genome-wide scan that is tailored to identify loci that are interconnected in pathways. We propose a scalable regression-based analytical framework to enable such a pathway-guided GWAS and demonstrate that it provides significant gains in power to detect disease associated SNPs. Our method requires two inputs, namely a) genome-wide summary level data (e.g., SNP p-values) and b) a grouping of genes into biologically meaningful categories (e.g., a database of pathways). It automatically adjusts the input p-values by incorporating the knowledge derived adaptively from the data and the pathways specified. The method involves a regularized logistic regression analysis to derive priors of each SNP and then re-weights the p-values of SNPs so as to maximize overall power of making discoveries. It increases the power to discover SNPs co-clustering into some of these pathways, while maintaining the global type-1 error (FWER) at the desired level. We used whole-genome simulations and summary data from real GWA studies of psoriasis, SLE, coronary artery disease and type-2 diabetes to illustrate the power improvement achieved by pathway-guided search. Our pipeline implemented as an R package can flexibly handle large number of prior annotations possibly derived from multiple databases.

genetics↗

bioRxiv2017

Stability of Commonly Used Haematological Parameters in Samples Stored at 33°C, 22°C and 4°C

AimThis study aimed to investigate the analytical bias and imprecision in haematological parameters induced by storage at 4{degrees}C, 22{degrees}C and 33 {degrees}C.\n\nMethodsThree K2EDTA anticoagulated vials of blood were collected from each of twenty blood donors and stored at 4{degrees}C, 22{degrees}C and 33{degrees}C respectively. Readings from each vial were taken at 0, 4, 6, 12, 24, 48 and 72 hours after collection on the Sysmex XP-100 analyser. The mean and median shift of the parameters relative to the baseline and the coefficient of variation for each time-temperature combination were calculated. The shift was compared to the maximum acceptable bias.\n\nResultsHaemoglobin, Red Blood Cell Count, White Blood Cell Count, Mean Corpuscular Haemoglobin were stable for at least twenty four hours at 33{degrees}C. Haematocrit, Mean Corpuscular Volume and Platelet Counts were stable for less than four hours at 33{degrees}C. All the above parameters were stable for longer at 22{degrees}C and 4{degrees}C. The three-part differential count showed instability within four hours at 33 {degrees}C.\n\nConclusionsStrict pre-analytical control is needed at 33{degrees}C or above due to the marked instability of most parameters. However, Haemoglobin, Red Blood Cell Count, White Blood Cell Count and Mean Corpuscular Haemoglobin remain relatively stable even at 33{degrees}C.\n\nKey MessageHaematology samples exposed to temperatures of 33{degrees}C or above show rapid change in MCV, HCT,MCHC, RDW, Platelet Counts and three-part differential counts. Settings where prolonged exposure to these temperatures cannot be avoided should rely on the more stable parameters of Haemoglobin, RBC Counts, MCH and WBC Counts.

pathology↗