Biology subjects

Malladi, V.

Publications and source records attributed to Malladi, V..

4 recordsLinked to original sources

bioRxiv2024

Optimizing genomics pipeline execution with integer linear programming

AO_SCPLOWBSTRACTC_SCPLOWIn the field of genomics, bioinformatics pipelines play a crucial role in processing and analyzing vast biological datasets. These pipelines, consisting of interconnected tasks, can be optimized for efficiency and scalability by leveraging cloud platforms such as Microsoft Azure. The choice of compute resources introduces a trade-off between cost and time. This paper introduces an approach that uses Linear Programming (LP) to optimize pipeline execution. We consider optimizing two competing cases: minimizing cost with a run duration restriction and minimizing duration with a cost restriction. Our results showcase the utility of using LP in guiding researchers to make informed compute decisions based on specific data sets, cost and time requirements, and resource constraints.

bioinformatics↗

bioRxiv2021

Enhancer and super-enhancer landscape in polycystic kidney disease

Widespread aberrant gene expression is pathological hallmark of polycystic kidney disease (PKD). Numerous pathogenic signaling cascades, including c-Myc, Fos, and Jun are transactivated. However, the underlying epigenetic regulators are poorly defined. Here we show that H3K27ac, a histone modification that marks active enhancers, is elevated in mouse and human ADPKD samples. Using comparative H3K27ac ChIP-Seq analysis, we mapped >16000 active intronic and intergenic enhancer elements in Pkd1-mutant mouse kidneys. We find that the cystic kidney epigenetic landscape resembles that of a developing kidney, and >90% of upregulated genes in Pkd1-mutant kidneys are co-housed with activated enhancers in the same topologically associated domains. Furthermore, we identify an evolutionarily-conserved enhancer cluster downstream of the c-Myc gene and super-enhancers flanking both Jun and Fos loci in mouse and human ADPKD models. Deleting these regulatory elements reduces c-Myc, Jun, or Fos abundance and suppresses proliferation and 3D cyst growth of Pkd1-mutant cells. Finally, inhibiting glycolysis and glutaminolysis or activating Ppara in Pkd1-mutant cells lowers global H3K27ac levels and on c-Myc enhancers. Thus, our work suggests that epigenetic rewiring mediates the transcriptomic dysregulation in PKD, and the regulatory elements can be targeted to slow cyst growth.

molecular biology↗

bioRxiv2021

TReNCo: Topologically associating domain (TAD) aware regulatory network construction

There has long been a desire to understand, describe, and model gene regulatory networks controlling numerous biologically meaningful processes like differentiation. Despite many notable improvements to models over the years, many models do not accurately capture subtle biological and chemical characteristics of the cell such as high-order chromatin domains of the chromosomes. Topologically Associated Domains (TAD) are one of these genomic regions that are enriched for contacts within themselves. Here we present TAD-aware Regulatory Network Construction or TReNCo, a memory-lean method utilizing epigenetic marks of enhancer and promoter activity, and gene expression to create context-specific transcription factor-gene regulatory networks. TReNCo utilizes common assays, ChIP-seq, RNA-seq, and TAD boundaries as a hard cutoff, instead of distance based, to efficiently create context-specific TF-gene regulatory networks. We used TReNCo to define the enhancer landscape and identify transcription factors (TFs) that drive the cardiac development of the mouse. Our results show that we are able to build specialized adjacency regulatory network graphs containing biologically relevant connections and time dependent dynamics.

bioinformatics↗

bioRxiv2020

An integrated genomics approach towards deciphering human genome codes shaping HIV-1 proviral transcription and fate

A large body of work has revealed fundamental principles of HIV-1 integration into the human genome. However, the effect of the integration site to proviral transcription activity has so far remained elusive. Here we combine open-source, large-scale datasets including epigenetics, transcriptome, and 3D genome architecture to interrogate the chromatin states, transcription activity landscape, and nuclear sub-compartments around HIV-1 integration sites in CD4+ T cells to decipher human genome codes shaping the transcription of proviral classes defined based on their position and orientation in the genome. Using a Hidden Markov Model, we describe the importance of specific chromatin states and genome architecture in the control of HIV-1 transcription activity. Additionally, implementation of a machine-learning logistic regression model reveals upstream chromatin accessibility, transcription activity, and categorical nuclear sub-compartments as optimal features predicting HIV-1 transcriptional outcomes. We finally demonstrate clinical relevance by interrogating the positions of intact proviruses persisting in patients under suppressive therapy and provide a compass compatible with clinical decision-making.

genomics↗