Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes.

Understanding the biological roles of all genes only through experimental methods is challenging. A computational approach with reliable interpretability is needed to infer the function of genes, particularly for non-coding RNAs. We have analyzed genomic features that are present across both coding and non-coding genes like transcription factor (TF) and cofactor ChIP-seq (823), histone modifications ChIP-seq (n = 621), cap analysis gene expression (CAGE) tags (n = 255), and DNase hypersensitivity profiles (n = 255) to predict ontology-based functions of genes.

Matching queried single-cell open-chromatin profiles to large pools of single-cell transcriptomes and epigenomes for reference supported analysis.

The true benefits of large single-cell transcriptome and epigenome data sets can be realized only with the development of new approaches and search tools for annotating individual cells. Matching a single-cell epigenome profile to a large pool of reference cells remains a major challenge. Here, we present scEpiSearch, which enables searching, comparison, and independent classification of single-cell open-chromatin profiles against a large reference of single-cell expression and open-chromatin data sets.

Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs.

Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy.

Improving Chromatin-Interaction Prediction Using Single-Cell Open-Chromatin Profiles and Making Insight Into the -Regulatory Landscape of the Human Brain.

Single-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell type. However, currently available -regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin interactions despite the fact that long-range interactions among genomic sites play a significant role in gene regulation. Here, we propose a method that predicts both short and long-range interactions among genomic sites using single-cell open chromatin profiles.

FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles.

The advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA, cause high drop-out rate and noise in single-cell open-chromatin profiles. We introduce here a robust method called as forest of imputation trees (FITs) to recover original signals from highly sparse and noisy single-cell open-chromatin profiles.

Correction to: Transcriptome-wide identification of genes involved in Ascorbate-Glutathione cycle (Halliwell-Asada pathway) and related pathway for elucidating its role in antioxidative potential in finger millet ( (L.)).

[This corrects the article DOI: 10.1007/s13205-018-1511-9.].

Transcriptome-wide identification of genes involved in Ascorbate-Glutathione cycle (Halliwell-Asada pathway) and related pathway for elucidating its role in antioxidative potential in finger millet ( (L.)).

Finger millet is being recognized as a potential future crop due to their nutrient contents and antioxidative properties, which are much higher compared to the other minor millets for providing health benefits. The synthesis of these nutritional components is governed by the expression of several gene(s). Therefore, it is necessary to characterize these genes for understanding the molecular mechanisms behind de novo synthesis of nutrient components.

Modeling of the jasmonate signaling pathway in Arabidopsis thaliana with respect to pathophysiology of Alternaria blight in Brassica.

The productivity of Oilseed Brassica, one of the economically important crops of India, is seriously affected by the disease, Alternaria blight. The disease is mainly caused by two major necrotrophic fungi, Alternaria brassicae and Alternaria brassicicola which are responsible for significant yield losses. Till date, no resistant source is available against Alternaria blight, hence plant breeding methods can not be used to develop disease resistant varieties.

Domain analysis of 3 Keto Acyl-CoA synthase for structural variations in Vitis vinifera and Oryza brachyantha using comparative modelling.

The long chain fatty acids incorporated into plant lipids are derived from the iterative addition of C2 units which is provided by malonyl-CoA to an acyl-CoA after interactions with 3-ketoacyl-CoA synthase (KCS), found in several plants. This study provides functional characterization of three 3 ketoacyl CoA synthase like proteins in Vitis vinifera (one) and Oryza brachyantha (two proteins). Sequence analysis reveals that protein of Oryza brachyantha shows 96% similarity to a hypothetical protein in Sorghum bicolor; total 11 homologs were predicted in Sorghum bicolor.