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The PARIGA server for real time filtering and analysis of reciprocal BLAST results.

Orsini M, Carcangiu S, Cuccuru G, Uva P, Tramontano A - PLoS ONE (2013)

Bottom Line: These applications span from simple tasks such as mapping sequences over a database to more complex procedures as clustering or annotation processes.The Pariga web server is designed to be a helpful tool for managing the results of sequence similarity searches.The design and implementation of the server renders all operations very fast and easy to use.

View Article: PubMed Central - PubMed

Affiliation: CRS4 Bioinformatics Laboratory, Science and Technology Park Polaris, Pula, Italy. orsini@crs4.it

ABSTRACT
BLAST-based similarity searches are commonly used in several applications involving both nucleotide and protein sequences. These applications span from simple tasks such as mapping sequences over a database to more complex procedures as clustering or annotation processes. When the amount of analysed data increases, manual inspection of BLAST results become a tedious procedure. Tools for parsing or filtering BLAST results for different purposes are then required. We describe here PARIGA (http://resources.bioinformatica.crs4.it/pariga/), a server that enables users to perform all-against-all BLAST searches on two sets of sequences selected by the user. Moreover, since it stores the two BLAST output in a python-serialized-objects database, results can be filtered according to several parameters in real-time fashion, without re-running the process and avoiding additional programming efforts. Results can be interrogated by the user using logical operations, for example to retrieve cases where two queries match same targets, or when sequences from the two datasets are reciprocal best hits, or when a query matches a target in multiple regions. The Pariga web server is designed to be a helpful tool for managing the results of sequence similarity searches. The design and implementation of the server renders all operations very fast and easy to use.

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Related in: MedlinePlus

Pariga logical schema.Central columns represent the original input files, while results are indicated in the columns on the side. Boxes indicate logical operations that can be performed on the results. As an example: COMMON: which sequence(s) of the dataset B is(are) shared in BLAST results of sequence A2 and A3 of the dataset A? CROSS: once sequence A1 is selected from dataset A, in which results of the dataset B does it appear? MULTIPLE: which sequence of dataset A appears more than once (i.e. matches more than one region) in the results of sequence B2 of dataset B?
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pone-0062224-g002: Pariga logical schema.Central columns represent the original input files, while results are indicated in the columns on the side. Boxes indicate logical operations that can be performed on the results. As an example: COMMON: which sequence(s) of the dataset B is(are) shared in BLAST results of sequence A2 and A3 of the dataset A? CROSS: once sequence A1 is selected from dataset A, in which results of the dataset B does it appear? MULTIPLE: which sequence of dataset A appears more than once (i.e. matches more than one region) in the results of sequence B2 of dataset B?

Mentions: The two datasets can be uploaded by the user who can select the desired traditional BLAST parameters (word size, expected e-value, gap-open and extension, etc.). The visualization of each of the BLAST results is very similar to the standard tabular BLAST output with the possibility of retrieving the alignment by clicking on the entry name. Summary table, statistics and a graphical summary of matches can be viewed by clicking on the appropriate icon in the table header. Two additional columns have been added to the traditional schema, named coverage and inv-cov. These indicate the fraction of the query and the subject involved in the alignment, respectively. Two novel key functionalities have been implemented. First of all, the results can be visualized and filtered in real time according to parameters such as similarity, coverage, e-value etc. (Figure 1, left panel). Second, since results are stored in a hidden database structure, the user can easily perform logical operations on them by simply selecting one of the options COMMON, CROSS and MULTIPLE (Figure 1, right panel, and schema in Figure 2).


The PARIGA server for real time filtering and analysis of reciprocal BLAST results.

Orsini M, Carcangiu S, Cuccuru G, Uva P, Tramontano A - PLoS ONE (2013)

Pariga logical schema.Central columns represent the original input files, while results are indicated in the columns on the side. Boxes indicate logical operations that can be performed on the results. As an example: COMMON: which sequence(s) of the dataset B is(are) shared in BLAST results of sequence A2 and A3 of the dataset A? CROSS: once sequence A1 is selected from dataset A, in which results of the dataset B does it appear? MULTIPLE: which sequence of dataset A appears more than once (i.e. matches more than one region) in the results of sequence B2 of dataset B?
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3646873&req=5

pone-0062224-g002: Pariga logical schema.Central columns represent the original input files, while results are indicated in the columns on the side. Boxes indicate logical operations that can be performed on the results. As an example: COMMON: which sequence(s) of the dataset B is(are) shared in BLAST results of sequence A2 and A3 of the dataset A? CROSS: once sequence A1 is selected from dataset A, in which results of the dataset B does it appear? MULTIPLE: which sequence of dataset A appears more than once (i.e. matches more than one region) in the results of sequence B2 of dataset B?
Mentions: The two datasets can be uploaded by the user who can select the desired traditional BLAST parameters (word size, expected e-value, gap-open and extension, etc.). The visualization of each of the BLAST results is very similar to the standard tabular BLAST output with the possibility of retrieving the alignment by clicking on the entry name. Summary table, statistics and a graphical summary of matches can be viewed by clicking on the appropriate icon in the table header. Two additional columns have been added to the traditional schema, named coverage and inv-cov. These indicate the fraction of the query and the subject involved in the alignment, respectively. Two novel key functionalities have been implemented. First of all, the results can be visualized and filtered in real time according to parameters such as similarity, coverage, e-value etc. (Figure 1, left panel). Second, since results are stored in a hidden database structure, the user can easily perform logical operations on them by simply selecting one of the options COMMON, CROSS and MULTIPLE (Figure 1, right panel, and schema in Figure 2).

Bottom Line: These applications span from simple tasks such as mapping sequences over a database to more complex procedures as clustering or annotation processes.The Pariga web server is designed to be a helpful tool for managing the results of sequence similarity searches.The design and implementation of the server renders all operations very fast and easy to use.

View Article: PubMed Central - PubMed

Affiliation: CRS4 Bioinformatics Laboratory, Science and Technology Park Polaris, Pula, Italy. orsini@crs4.it

ABSTRACT
BLAST-based similarity searches are commonly used in several applications involving both nucleotide and protein sequences. These applications span from simple tasks such as mapping sequences over a database to more complex procedures as clustering or annotation processes. When the amount of analysed data increases, manual inspection of BLAST results become a tedious procedure. Tools for parsing or filtering BLAST results for different purposes are then required. We describe here PARIGA (http://resources.bioinformatica.crs4.it/pariga/), a server that enables users to perform all-against-all BLAST searches on two sets of sequences selected by the user. Moreover, since it stores the two BLAST output in a python-serialized-objects database, results can be filtered according to several parameters in real-time fashion, without re-running the process and avoiding additional programming efforts. Results can be interrogated by the user using logical operations, for example to retrieve cases where two queries match same targets, or when sequences from the two datasets are reciprocal best hits, or when a query matches a target in multiple regions. The Pariga web server is designed to be a helpful tool for managing the results of sequence similarity searches. The design and implementation of the server renders all operations very fast and easy to use.

Show MeSH
Related in: MedlinePlus