Search PubMed⌕ Search

PubMed · 11005789

Decoding the human genome sequence.

Abstract

The year 2000 is marked by the production of the sequence of the human genome. A 'working draft' of high quality sequence covering 90% of the genome has been determined and a quarter is in finished form, including the first two completed chromosomes. All sequence data from the project is made freely available to the community via the Internet, for further analysis and exploitation. The challenge which lies ahead is to decipher the information. Knowledge of the human genome sequence will enable us to understand how the genetic information determines the development, structure and function of the human body. We will be able to explore how variations within our DNA sequence cause disease, how they affect our interaction with our environment and ultimately to develop new and effective ways to improve human health.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D R Bentley. 2000. Decoding the human genome sequence.. https://doi.org/10.1093/hmg%2F9.16.2353

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.

MOTIVATION: Genomic sequence-to-activity models can decipher gene regulatory mechanisms and predict the functional impact of regulatory variants. However, current models struggle to integrate information from sequences outside promoters, especially information from cell type specific regulatory elements. RESULTS: Here, we propose incorporating base-pair resolution evolutionary conservation data into genomic sequence-to-expression predictors. We explore two training strategies-training from scratch or fine-tuning an existing sequence-only model with additional conservation input. We find that in both cases, base-pair resolution conservation data improves cell type specific sequence-to-expression prediction, with training from scratch yielding the greatest benefit. The improvement in cell type specific expression prediction can be attributed in part to the fact that models trained on sequence and conservation data learn to better recognize cell type specific regulatory elements than models trained on sequence alone. AVAILABILITY: Code is available at https://github.com/ni-lab/basenji-phyloP.

Conserved Sequence↗

Lift&Add-rapid and robust addition of new species to alignments of conserved non-coding sequences.

MOTIVATION: Identifying sequence constraint across long evolutionary distances is a powerful method for the discovery of functional genomic sequences, especially putative non-coding elements. Conserved elements have been a mainstay of comparative genomic research, and can be further investigated for species-specific sequence acceleration to dissect the genetic basis of trait evolution. The conclusions of these comparative genomic studies are contingent on the number and range of species included in this phylogenetic analysis. However, while the number of metazoan genomes sequences is increasing rapidly, adding new genomes to existing whole-genome alignments remains computationally expensive. RESULTS: Here, we present a bioinformatic workflow, Lift&Add, that enables conserved elements, coding or non-coding, to be rapidly mapped to new genomes ("Lift") and subsequently be added to pre-existing multiple species alignments ("Add"), thus providing an avenue for easy exploration of these putative functional elements. Focusing here on a group of species that has been largely under-represented in genomic comparisons, the marsupials, we demonstrate the intuition behind this workflow and provide an example comparative genomic analysis that can be performed. IMPLEMENTATION AND AVAILABILITY: Lift&Add is implemented as a series of scripts in Snakemake and bash, which can be downloaded from https://github.com/navyashukladr/Lift_and_Add.

Conserved Sequence↗

Evolution of gene order conservation in prokaryotes.

BACKGROUND: As more complete genomes are sequenced, conservation of gene order between different organisms is emerging as an informative property of the genomes. Conservation of gene order has been used for predicting function and functional interactions of proteins, as well as for studying the evolutionary relationships between genomes. The reasons for the maintenance of gene order are still not well understood, as the organization of the prokaryote genome into operons and lateral gene transfer cannot possibly account for all the instances of conservation found. Comprehensive studies of gene order are one way of elucidating the nature of these maintaining forces. RESULTS: Gene order is extensively conserved between closely related species, but rapidly becomes less conserved among more distantly related organisms, probably in a cooperative fashion. This trend could be universal in prokaryotic genomes, as archaeal genomes are likely to behave similarly to bacterial genomes. Gene order conservation could therefore be used as a valid phylogenetic measure to study relationships between species. Even between very distant species, remnants of gene order conservation exist in the form of highly conserved clusters of genes. This suggests the existence of selective processes that maintain the organization of these regions. Because the clusters often span more than one operon, common regulation probably cannot be invoked as the cause of the maintenance of gene order. CONCLUSIONS: Gene order conservation is a genomic measure that can be useful for studying relationships between prokaryotes and the evolutionary forces shaping their genomes. Gene organization is extensively conserved in some genomic regions, and further studies are needed to elucidate the reason for this conservation.

Conserved Sequence↗