Sharmistha Majumdar

The paper identifies non-canonical proteins encoded by non-coding ORFs in Alzheimer's disease, finding 67 differentially expressed proteins in the fusiform gyrus.

The paper identifies THAP9, a domesticated DNA transposon-derived gene, as dynamically regulated during human germline and early embryonic development, with lineage-specific expression patterns and epigenomic changes.

This study examines the evolutionary conservation of an RNaseH-like domain in the THAP9 family, showing that catalytic framework is conserved while peripheral regions diversify, and that conserved motifs are critical for DNA integration.

This study investigates the evolutionary and functional role of the C-terminal domain in THAP9 and DmTNP, showing conserved structural elements and a non-essential role in DNA transposition.

The paper investigates whether the antisense lncRNA THAP9-AS1 can bind to the product of its sense pair, THAP9, by analyzing G-quadruplex forming sequences and validating their structural importance.

This paper uses machine learning to integrate multi-omics data from HNSCC patients, identifying biomarkers with high classification accuracy.

This study identifies THAP9 as a human-specific regulator of oligodendrocyte maturation through transcriptomic analysis of human oligodendrocyte progenitor cells and mature oligodendrocytes.

The paper analyzes THAP9 and THAP9-AS1 across multiple cancers to show their expression correlates with poor patient survival and identifies coexpressed genes involved in DNA binding and viral infection.

The paper performs evolutionary analysis and in silico characterization of THAP9, identifying conserved regions, novel motifs, and potential cancer-related expression patterns.

This study identifies a conserved DDE motif in hTHAP9's catalytic domain, essential for DNA cleavage, and finds a residue mutation that increases transposition activity.

The paper examines the differential expression of THAP9 and THAP9-AS1 during the S-phase of the cell cycle under various stress conditions.

This study analyzes sequence and structural variations in the THAP domains of human THAP proteins and their homologs, identifying structural hotspots and sequence length variations.

This study compares ChIP-exo peak-callers, finding that self-learning algorithms outperform peak-pair-based methods and that tools accounting for indirect binding provide more comprehensive binding mode information.

The paper introduces SMA, an algorithm to predict G domains in G proteins by matching G-box patterns and inter-box spacer lengths, improving precision for family-specific motifs.