Biomedical subjects

Robert C Edgar

Publications and source records attributed to Robert C Edgar.

4 recordsLinked to original sources

PubMed2025

Logan: Planetary-Scale Genome Assembly Surveys Life's Diversity.

The breadth of life's diversity is unfathomable, but public nucleic acid sequencing data offers a window into the dispersion and evolution of genetic diversity across Earth. However the rapid growth and accumulation of sequence data have outpaced efficient analysis capabilities. The largest collection of freely available sequencing data is the Sequence Read Archive (SRA), comprising 27.3 million datasets or 5 × 1016 basepairs. To realize the potential of the SRA, we constructed Logan, a massive sequence assembly transforming short reads into long contigs and compressing the data over 100-fold, enabling highly efficient petabase-scale analysis. We created Logan-Search, a k-mer index of Logan for free planetary-scale sequence search, returning matches in minutes. We used Logan contigs to identify >200 million plastic-degrading enzyme homologs, and validate novel enzymes with catalytic activities exceeding current reference standards. Further, we vastly expand the known diversity of proteins (30-fold over UniRef50), plasmids (22-fold over PLSDB), P4 satellites (4.5-fold), and the recently described Obelisk RNA elements (3.7-fold). Logan also enables ecological and biomedical data mining, such as global tracking of antimicrobial resistance genes and the characterization of viral reactivation across millions of human BioSamples. By transforming the SRA, Logan democratizes access to the world's public genetic data and opens frontiers in biotechnology, molecular ecology, and global health.

Journal Article↗

PubMed2003

Using motif-based methods in multiple genome analyses: a case study comparing orthologous mesophilic and thermophilic proteins.

Protein motifs represent highly conserved regions within protein families and are generally accepted to describe critical regions required for protein stability and/or function. In this comprehensive analysis, we present a robust, unique approach to identify and compare corresponding mesophilic and thermophilic sequence motifs between all orthologous proteins within 44 microbial genomes. Motif similarity is determined through global sequence alignment of mesophilic and thermophilic motif pairs, which are identified by a greedy algorithm. Our results reveal only modest correlation between motif and overall sequence similarity, highlighting the rationale of motif-based approaches in comprehensive multigenome comparisons. Conserved mutations reflect previously suggested physiochemical principles for conferring thermostability. Additionally, comparisons between corresponding mesophilic and thermophilic motif pairs provide key biochemical insights related to thermostability and can be used to test the evolutionary robustness of individual structural comparisons. We demonstrate the ability of our unique approach to provide key insights in two examples: the TATA-box binding protein and glutamate dehydrogenase families. In the latter example, conserved mutations hint at novel origins leading to structural stability differences within the hexamer structures. Additionally, we present amino acid composition data and average protein length comparisons for all 44 microbial genomes.

Amino Acid Motifs↗

PubMed2003

SATCHMO: sequence alignment and tree construction using hidden Markov models.

MOTIVATION: Aligning multiple proteins based on sequence information alone is challenging if sequence identity is low or there is a significant degree of structural divergence. We present a novel algorithm (SATCHMO) that is designed to address this challenge. SATCHMO simultaneously constructs a tree and a set of multiple sequence alignments, one for each internal node of the tree. The alignment at a given node contains all sequences within its sub-tree, and predicts which positions in those sequences are alignable and which are not. Aligned regions therefore typically get shorter on a path from a leaf to the root as sequences diverge in structure. Current methods either regard all positions as alignable (e.g. ClustalW), or align only those positions believed to be homologous across all sequences (e.g. profile HMM methods); by contrast SATCHMO makes different predictions of alignable regions in different subgroups. SATCHMO generates profile hidden Markov models at each node; these are used to determine branching order, to align sequences and to predict structurally alignable regions. RESULTS: In experiments on the BAliBASE benchmark alignment database, SATCHMO is shown to perform comparably to ClustalW and the UCSC SAM HMM software. Results using SATCHMO to identify protein domains are demonstrated on potassium channels, with implications for the mechanism by which tumor necrosis factor alpha affects potassium current. AVAILABILITY: The software is available for download from http://www.drive5.com/lobster/index.htm

Algorithms↗

PubMed2003

Simultaneous sequence alignment and tree construction using hidden Markov models.

We present a new algorithm (SATCHMO) that simultaneously estimates a tree and generates a set of multiple sequence alignments given a set of protein sequences. Alignments are constructed for each node in the tree. These alignments predict the structurally conserved elements of the sequences in a subtree and are therefore of different lengths, and represent different amino acid preferences, at different nodes. Hidden Markov Models (HMMs) are also generated for each node and are used to determine branching order, to align sequences and to predict structurally alignable regions. In experiments on the BAliBASE benchmark alignment database, SATCHMO is shown to perform comparably to ClustalW and the UCSC SAM HMM software. Results using SATCHMO to identify protein domains are demonstrated on potassium channels, with implications for the mechanism by which tumor necrosis factor alpha affects potassium current.

Algorithms↗