Biology subjects

Annaldasula, S.

Publications and source records attributed to Annaldasula, S..

3 recordsLinked to original sources

bioRxiv2026

commonPeak: Equivalence testing to identify common ChIP-seq peaks across conditions and protocols

BackgroundNew ChIP-seq protocols are increasingly adopted alongside established workflows. However, dedicated methods are lacking to quantify the agreement of peak location and intensity across datasets, including comparisons across protocols and biological conditions. ObjectivesWe present commonPeak, a statistical framework that identifies shared peaks across samples and tests whether their enrichment is similar across conditions, thereby supporting bench-marking of ChIP-seq protocols and cross-condition comparisons. ResultscommonPeak operates on BED peak sets and BAM files. As a use case, we applied it to an estrogen receptor alpha (ER) ChIP-seq dataset from tamoxifen-sensitive and -resistant breast cancer cell line MCF-7 cells and identified 225 shared peaks with significantly similar enrichment. These peaks were largely distinct from differentially bound sites and enriched for estrogen signaling. This illustrates how equivalence-based peak selection can help separate conserved ER-driven programs from condition-specific changes in ER-targeting endocrine drugs such as tamoxifen. Availability and implementationcommonPeak is freely available for non-commercial use via GitHub, with documentation and usage examples.

genomics↗

bioRxiv2022

Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements

Vocal learning, the ability to modify vocal behavior based on experience, is a convergently evolved trait in birds and mammals. To identify genomic elements associated with vocal learning, we integrated new experiments conducted in the brain of the Egyptian fruit bat with analyses of the genomes of 222 placental mammals. We first identified an anatomically specialized region of the bat motor cortex containing direct monosynaptic projections to laryngeal motoneurons. Using wireless neural recordings of this brain region in freely vocalizing bats, we verified that single neuron activity in this region relates to vocal production. We profiled the open chromatin of this vocal-motor region, which we used to train machine learning models to identify enhancers associated with vocal learning across mammals. We found 201 proteins and 45 candidate enhancers that display convergent evolution associated with vocal learning, many of which overlapped loci associated with human speech disability. One such locus contains the neurodevelopmental transcription factors TSHZ3 and ZNF536 and multiple candidate vocal learning-associated enhancers, suggesting the co-evolution of protein and regulatory sequences underlying vocal learning. One-Sentence SummaryAnalyses of bat neural activity and epigenomic data in a brain region involved in vocal behavior were used to identify proteins and regulatory elements associated with vocal learning in mammals.

neuroscience↗

bioRxiv2020

Network Analysis and Human Single Cell Brain Transcriptomics Reveal Novel Aspects of Alpha-Synuclein (SNCA) Biology

Alpha-synuclein (SNCA) aggregates are pathological hallmarks of synucleinopathies, neurodegenerative disorders including Parkinsons Disease (PD) and Lewy Body Dementia (LBD). Functional networks are not yet well-characterized for SNCA by CNS cell type. We investigated cell-specific differences in SNCA expression using Allen Brain Database single-nucleus RNA-seq data from human Middle Temporal Gyrus (MTG, 15,928 nuclei) and Anterior Cingulate Cortex (ACC, 7,258 nuclei). Weighted gene co-expression analysis (WGCNA) and hierarchical clustering identified a conserved SNCA co-expression module. Module genes were highly conserved (p < 10-10) and most highly expressed in excitatory neurons versus inhibitory neurons and other glial cells. SNCA co-expression module genes from ACC and MTG regions were then used to construct a protein-protein interaction (PPI) network, with SNCA empirically top hub. Genes in the SNCA PPI network were compared with genes nearest single nucleotide polymorphisms linked with PD risk in genome-wide association studies. 16 genes in our PPI network are nearest genes to PD risk loci (p < 0.0006) and 55 genes map within 100kb. Selected SNCA PPI network genes nearest PD risk loci were disrupted by CRISPR knock out gene editing for validation of network functional significance; disruption of STK39, GBA, and MBNL2 resulted in significantly elevated intracellular SNCA expression.

neuroscience↗