The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.

/pdf/a-gene-atlas-of-the-mouse-and-human-protein-encoding-2t2esz2vz6.pdf

A gene atlas of the mouse and human protein-encoding transcriptomes

The Human Gene Mutation Database (HGMD) constitutes a comprehensive core collection of data on germ-line mutations in nuclear genes underlying or associated with human inherited disease (www.hgmd.org). Data catalogued includes: single base-pair substitutions in coding, regulatory and splicing-relevant regions; micro-deletions and micro-insertions; indels; triplet repeat expansions as well as gross deletions; insertions; duplications; and complex rearrangements. Each mutation is entered into HGMD only once in order to avoid confusion between recurrent and identical-by-descent lesions. By March 2003, the database contained in excess of 39,415 different lesions detected in 1,516 different nuclear genes, with new entries currently accumulating at a rate exceeding 5,000 per annum. Since its inception, HGMD has been expanded to include cDNA reference sequences for more than 87% of listed genes, splice junction sequences, disease-associated and functional polymorphisms, as well as links to data present in publicly available online locus-specific mutation databases. Although HGMD has recently entered into a licensing agreement with Celera Genomics (Rockville, MD), mutation data will continue to be made freely available via the Internet.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/humu.10212

Human Gene Mutation Database (HGMD): 2003 update.

The Paragon Algorithm, a Next Generation Search Engine That Uses Sequence Temperature Values and Feature Probabilities to Identify Peptides from Tandem Mass Spectra

The volume of published biomedical research, and therefore the underlying biomedical knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers in coping with this information overload are text mining and knowledge extraction. Significant progress has been made in applying text mining to named entity recognition, text classification, terminology extraction, relationship extraction and hypothesis generation. Several research groups are constructing integrated flexible text-mining systems intended for multiple uses. The major challenge of biomedical text mining over the next 5–10 years is to make these systems useful to biomedical researchers. This will require enhanced access to full text, better understanding of the feature space of biomedical literature, better methods for measuring the usefulness of systems to users, and continued cooperation with the biomedical research community to ensure that their needs are addressed.

/pdf/a-survey-of-current-work-in-biomedical-text-mining-hawmeud9jp.pdf

A survey of current work in biomedical text mining

The PANTHER database was designed for high-throughput analysis of protein sequences. One of the key features is a simplified ontology of protein function, which allows browsing of the database by biological functions. Biologist curators have associated the ontology terms with groups of protein sequences rather than individual sequences. Statistical models (Hidden Markov Models, or HMMs) are built from each of these groups. The advantage of this approach is that new sequences can be automatically classified as they become available. To ensure accurate functional classification, HMMs are constructed not only for families, but also for functionally distinct subfamilies. Multiple sequence alignments and phylogenetic trees, including curator-assigned information, are available for each family. The current version of the PANTHER database includes training sequences from all organisms in the GenBank non-redundant protein database, and the HMMs have been used to classify gene products across the entire genomes of human, and Drosophila melanogaster. PANTHER is publicly available on the web at http://panther.celera.com.

PANTHER: A browsable database of gene products organized by biological function, using curated protein family and subfamily classification

The Celera Discovery System (CDS) is a web-accessible research workbench for mining genomic and related biological information Users have access to the human and mouse genome sequences with annotation presented in summary form in BioMolecule Reports for genes, transcripts and proteins Over 40 additional databases are available, including sequence, mapping, mutation, genetic variation, mRNA expression, protein structure, motif and classification data Data are accessible by browsing reports, through a variety of interactive graphical viewers, and by advanced query capability provided by the LION SRS search engine A growing number of sequence analysis tools are available, including sequence similarity, pattern searching, multiple sequence alignment and Hidden Markov Model search A user workspace keeps track of queries and analyses CDS is widely used by the academic research community and requires a subscription for access The system and academic pricing information are available at http://cdsceleracom

/pdf/the-celera-discovery-system-1mhhhhvs3x.pdf

The Celera Discovery System.

Below we describe the winning system that we built for the KDD Cup 2002 Task 1 competition Our system is a Rule-based Information Extraction (IE) system It combines pattern matching, Natural Language Processing (NLP) tools, semantic constraints based on the domain and the specific task, and a post-processing stage for making the final curation decision based on the various evidence (positive and negative) found within the document Development and implementation were made using the DIAL IE language and the ClearLab development environment The results achieved were significantly superior than those achieved using categorization approaches

/pdf/rule-based-extraction-of-experimental-evidence-in-the-119402g0lv.pdf

Rule-based extraction of experimental evidence in the biomedical domain: the KDD Cup 2002 (task 1)

An online life science research environment and virtual community with a focus on design and analysis of biological experiments includes a life sciences laboratory system employing at least one networked computer system that defines a virtual research environment. Users access the system through a portal associated with the networked computer system(s). The virtual research environment has a data coupling mechanism by which the user designates a set of user-specified data for bioinformatics processing. A processor(s) associated with the networked computer system(s) performs bioinformatics services upon the user-specified data. In one embodiment, the data coupling mechanism enables transfer of the user-specified data to a memory space that is mediated or accessed by the processor performing the bioinformatics processing. Users may thus exploit bioinformatics processing resources that are not deployed on users' local computer environments, and to store and organize information relating to life sciences research in a secure, online workspace.

Charles Lawrence

Papers

The Celera Discovery System.

Rule-based extraction of experimental evidence in the biomedical domain: the KDD Cup 2002 (task 1)

Information System for Biological and Life Sciences Research