Biology subjects

Parameswaran, A.

Publications and source records attributed to Parameswaran, A..

2 recordsLinked to original sources

bioRxiv2020

DNA Encoded Glycan Libraries as a next-generation tool for the study of glycan-protein interactions

Interactions between glycans and glycan-binding proteins (GBPs) mediate diverse cellular functions, and therefore are of diagnostic and therapeutic significance. Current leading strategies for studying glycan-GBP interactions require specialized knowledge and instrumentation. In this study, we report a strategy for studying glycan-GBP interactions that uses PCR, qPCR and next-generation sequencing (NGS) technologies that are more routinely accessible. Our headpiece conjugation-code ligation (HCCL) strategy couples glycans with unique DNA codes that specify glycan sugar moieties and glycosidic linkages when sequenced. We demonstrate the technology by synthesizing a DNA encoded glycan library of 50 biologically relevant glycans (DEGL-50) and probing interactions against 25 target proteins including lectins and antibodies. Data show glycan-GPB interactions in solution that are consistent with lower content, lower throughput ELISA assays. Data further demonstrate how monovalent and multivalent headpieces can be used to increase glycan-GPB interactions and enrich signals while using smaller sample sizes. The flexibility of our modular HCCL strategy has potential for producing large glycan libraries, facilitating high content-high throughput glycan binding studies, and increasing access to lower cost glyco-analyses. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/017012v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@fb1c1dorg.highwire.dtl.DTLVardef@1f2c3ceorg.highwire.dtl.DTLVardef@11546e7org.highwire.dtl.DTLVardef@1e21a83_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2020

GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis

MotivationA common but critical task in genomic data analysis is finding features that separate and thereby help explain differences between two classes of biological objects, e.g., genes that explain the differences between healthy and diseased patients. As lower-cost, high-throughput experimental methods greatly increase the number of samples that are assayed as objects for analysis, computational methods are needed to quickly provide insights into high-dimensional datasets with tens of thousands of objects and features. ResultsWe develop an interactive exploration tool called GO_SCPLOWENVISAGEC_SCPLOW that rapidly discovers the most discriminative feature pairs that best separate two classes in a dataset, and displays the corresponding visualizations. Since quickly finding top feature pairs is computationally challenging, especially when the numbers of objects and features are large, we propose a suite of optimizations to make GO_SCPLOWENVISAGEC_SCPLOW more responsive and demonstrate that our optimizations lead to a 400X speedup over competitive baselines for multiple biological data sets. With this speedup, GO_SCPLOWENVISAGEC_SCPLOW enables the exploration of more large-scale datasets and alternate hypotheses in an interactive and interpretable fashion. We apply GO_SCPLOWENVISAGEC_SCPLOW to uncover pairs of genes whose transcriptomic responses significantly discriminate treatments of several chemotherapy drugs. AvailabilityFree webserver at http://genvisage.knoweng.org:443/ with source code at https://github.com/KnowEnG/Genvisage

bioinformatics↗