Dataset Information

Feature expressions: creating and manipulating sequence datasets.

ABSTRACT: Annotation of features, such as introns, exons and protein coding regions in GenBank/EMBL/DDBJ entries is now standardized through use of the Features Table (FT) language. The essence of the FT language is described by the relation 'expression-->sequence', meaning that each FT expression evaluates to a sequence. For example, the expression M74750:1..50 evaluates to the first 50 bases of the sequence with accession number M74750. Because FT is intrinsic to the database definition, it can serve as a software- and platform-independent lingua franca for sequence manipulation. The XYLEM package makes it possible to create and manipulate sequence datasets using FT expressions. FEATURES is a program that resolves FT expressions into their corresponding sequences. Annotated features can be retrieved either by feature key or by expression. Even unannotated portions of a sequence can be retrieved by user-generated FT expressions. Applications of the FT language include retrieval of subsequences from large sequence entries, generation of chromosome models or artificial DNA constructs, and representation of restriction maps or mutants.

SUBMITTER: Fristensky B

PROVIDER: S-EPMC310486 | biostudies-literature | 1993 Dec

REPOSITORIES: biostudies-literature

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Publications

Feature expressions: creating and manipulating sequence datasets.

Fristensky B B

Nucleic acids research 19931201 25

Annotation of features, such as introns, exons and protein coding regions in GenBank/EMBL/DDBJ entries is now standardized through use of the Features Table (FT) language. The essence of the FT language is described by the relation 'expression-->sequence', meaning that each FT expression evaluates to a sequence. For example, the expression M74750:1..50 evaluates to the first 50 bases of the sequence with accession number M74750. Because FT is intrinsic to the database definition, it can serve as ...[more]

PMID: 8290362

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Project description:The whole-genome sequence assembly (WGSA) problem is among one of the most studied problems in computational biology. Despite the availability of a plethora of tools (i.e., assemblers), all claiming to have solved the WGSA problem, little has been done to systematically compare their accuracy and power. Traditional methods rely on standard metrics and read simulation: while on the one hand, metrics like N50 and number of contigs focus only on size without proportionately emphasizing the information about the correctness of the assembly, comparisons performed on simulated dataset, on the other hand, can be highly biased by the non-realistic assumptions in the underlying read generator. Recently the Feature Response Curve (FRC) method was proposed to assess the overall assembly quality and correctness: FRC transparently captures the trade-offs between contigs' quality against their sizes. Nevertheless, the relationship among the different features and their relative importance remains unknown. In particular, FRC cannot account for the correlation among the different features. We analyzed the correlation among different features in order to better describe their relationships and their importance in gauging assembly quality and correctness. In particular, using multivariate techniques like principal and independent component analysis we were able to estimate the "excess-dimensionality" of the feature space. Moreover, principal component analysis allowed us to show how poorly the acclaimed N50 metric describes the assembly quality. Applying independent component analysis we identified a subset of features that better describe the assemblers performances. We demonstrated that by focusing on a reduced set of highly informative features we can use the FRC curve to better describe and compare the performances of different assemblers. Moreover, as a by-product of our analysis, we discovered how often evaluation based on simulated data, obtained with state of the art simulators, lead to not-so-realistic results.

Dataset Information

Feature expressions: creating and manipulating sequence datasets.

Publications

Feature expressions: creating and manipulating sequence datasets.

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