Multiple Vector Seeds for Protein Alignment View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2004

AUTHORS

Daniel G. Brown

ABSTRACT

We present a framework for improving local protein alignment algorithms. Specifically, we discuss how to extend local protein aligners to use a collection of vector seeds [3] to reduce noise hits. We model picking a set of vector seeds as an integer programming problem, and give algorithms to choose such a set of seeds. A good set of vector seeds we have chosen allows four times fewer false positive hits, while preserving essentially identical sensitivity as BLASTP. More... »

PAGES

170-181

References to SciGraph publications

Book

TITLE

Algorithms in Bioinformatics

ISBN

978-3-540-23018-2
978-3-540-30219-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-30219-3_15

DOI

http://dx.doi.org/10.1007/978-3-540-30219-3_15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004146474


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0601", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biochemistry and Cell Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Waterloo", 
          "id": "https://www.grid.ac/institutes/grid.46078.3d", 
          "name": [
            "School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brown", 
        "givenName": "Daniel G.", 
        "id": "sg:person.0642727740.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0642727740.54"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/nar/28.1.45", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004742321"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/18.3.440", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006017712"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/640075.640083", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018184175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-27801-6_4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019005750", 
          "https://doi.org/10.1007/978-3-540-27801-6_4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-27801-6_4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019005750", 
          "https://doi.org/10.1007/978-3-540-27801-6_4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcss.2003.04.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021213181"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcss.2003.04.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021213181"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcss.2003.04.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021213181"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0166-218x(03)00382-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023652568"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0166-218x(03)00382-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023652568"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-2836(81)90087-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024589839"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-39763-2_4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048701396", 
          "https://doi.org/10.1007/978-3-540-39763-2_4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-39763-2_4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048701396", 
          "https://doi.org/10.1007/978-3-540-39763-2_4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0219720004000326", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063004526"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0219720004000661", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063004556"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/974614.974625", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098879727"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004", 
    "datePublishedReg": "2004-01-01", 
    "description": "We present a framework for improving local protein alignment algorithms. Specifically, we discuss how to extend local protein aligners to use a collection of vector seeds [3] to reduce noise hits. We model picking a set of vector seeds as an integer programming problem, and give algorithms to choose such a set of seeds. A good set of vector seeds we have chosen allows four times fewer false positive hits, while preserving essentially identical sensitivity as BLASTP.", 
    "editor": [
      {
        "familyName": "Jonassen", 
        "givenName": "Inge", 
        "type": "Person"
      }, 
      {
        "familyName": "Kim", 
        "givenName": "Junhyong", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-30219-3_15", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-23018-2", 
        "978-3-540-30219-3"
      ], 
      "name": "Algorithms in Bioinformatics", 
      "type": "Book"
    }, 
    "name": "Multiple Vector Seeds for Protein Alignment", 
    "pagination": "170-181", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-30219-3_15"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "13c24aaddeac49401d91001e64713cb5712794350b8ffe2449fdce0ab7df4044"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1004146474"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-30219-3_15", 
      "https://app.dimensions.ai/details/publication/pub.1004146474"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T21:55", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8693_00000245.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-540-30219-3_15"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-30219-3_15'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-30219-3_15'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-30219-3_15'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-30219-3_15'


 

This table displays all metadata directly associated to this object as RDF triples.

105 TRIPLES      23 PREDICATES      38 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-30219-3_15 schema:about anzsrc-for:06
2 anzsrc-for:0601
3 schema:author Nbd7a720978e544a8bd61dab205df4a51
4 schema:citation sg:pub.10.1007/978-3-540-27801-6_4
5 sg:pub.10.1007/978-3-540-39763-2_4
6 https://doi.org/10.1016/0022-2836(81)90087-5
7 https://doi.org/10.1016/j.jcss.2003.04.002
8 https://doi.org/10.1016/s0166-218x(03)00382-2
9 https://doi.org/10.1093/bioinformatics/18.3.440
10 https://doi.org/10.1093/nar/28.1.45
11 https://doi.org/10.1142/s0219720004000326
12 https://doi.org/10.1142/s0219720004000661
13 https://doi.org/10.1145/640075.640083
14 https://doi.org/10.1145/974614.974625
15 schema:datePublished 2004
16 schema:datePublishedReg 2004-01-01
17 schema:description We present a framework for improving local protein alignment algorithms. Specifically, we discuss how to extend local protein aligners to use a collection of vector seeds [3] to reduce noise hits. We model picking a set of vector seeds as an integer programming problem, and give algorithms to choose such a set of seeds. A good set of vector seeds we have chosen allows four times fewer false positive hits, while preserving essentially identical sensitivity as BLASTP.
18 schema:editor N4836781ca56d441999046e76688c03e6
19 schema:genre chapter
20 schema:inLanguage en
21 schema:isAccessibleForFree true
22 schema:isPartOf Nd224c2c78d944c8cb13cee376919e503
23 schema:name Multiple Vector Seeds for Protein Alignment
24 schema:pagination 170-181
25 schema:productId N1faa4f7deb1f4ffc91c9bd00220fda34
26 N685a7af7e2c7425ea8e222ac8b42b32a
27 Nf37c6c9212454a9083484861f31f197b
28 schema:publisher N7706993e957c440189c66bcc0f6d0c53
29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004146474
30 https://doi.org/10.1007/978-3-540-30219-3_15
31 schema:sdDatePublished 2019-04-15T21:55
32 schema:sdLicense https://scigraph.springernature.com/explorer/license/
33 schema:sdPublisher N015d216aeeef452b9780a0b3274b8e18
34 schema:url http://link.springer.com/10.1007/978-3-540-30219-3_15
35 sgo:license sg:explorer/license/
36 sgo:sdDataset chapters
37 rdf:type schema:Chapter
38 N015d216aeeef452b9780a0b3274b8e18 schema:name Springer Nature - SN SciGraph project
39 rdf:type schema:Organization
40 N11f0ed7d23c54a6c87c6ae6dd901096d schema:familyName Kim
41 schema:givenName Junhyong
42 rdf:type schema:Person
43 N1faa4f7deb1f4ffc91c9bd00220fda34 schema:name dimensions_id
44 schema:value pub.1004146474
45 rdf:type schema:PropertyValue
46 N31d7f9e4f51147be95981bc43555b599 rdf:first N11f0ed7d23c54a6c87c6ae6dd901096d
47 rdf:rest rdf:nil
48 N4836781ca56d441999046e76688c03e6 rdf:first Nbb2e9e0965694629bac81102761278f3
49 rdf:rest N31d7f9e4f51147be95981bc43555b599
50 N685a7af7e2c7425ea8e222ac8b42b32a schema:name doi
51 schema:value 10.1007/978-3-540-30219-3_15
52 rdf:type schema:PropertyValue
53 N7706993e957c440189c66bcc0f6d0c53 schema:location Berlin, Heidelberg
54 schema:name Springer Berlin Heidelberg
55 rdf:type schema:Organisation
56 Nbb2e9e0965694629bac81102761278f3 schema:familyName Jonassen
57 schema:givenName Inge
58 rdf:type schema:Person
59 Nbd7a720978e544a8bd61dab205df4a51 rdf:first sg:person.0642727740.54
60 rdf:rest rdf:nil
61 Nd224c2c78d944c8cb13cee376919e503 schema:isbn 978-3-540-23018-2
62 978-3-540-30219-3
63 schema:name Algorithms in Bioinformatics
64 rdf:type schema:Book
65 Nf37c6c9212454a9083484861f31f197b schema:name readcube_id
66 schema:value 13c24aaddeac49401d91001e64713cb5712794350b8ffe2449fdce0ab7df4044
67 rdf:type schema:PropertyValue
68 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
69 schema:name Biological Sciences
70 rdf:type schema:DefinedTerm
71 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
72 schema:name Biochemistry and Cell Biology
73 rdf:type schema:DefinedTerm
74 sg:person.0642727740.54 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
75 schema:familyName Brown
76 schema:givenName Daniel G.
77 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0642727740.54
78 rdf:type schema:Person
79 sg:pub.10.1007/978-3-540-27801-6_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019005750
80 https://doi.org/10.1007/978-3-540-27801-6_4
81 rdf:type schema:CreativeWork
82 sg:pub.10.1007/978-3-540-39763-2_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048701396
83 https://doi.org/10.1007/978-3-540-39763-2_4
84 rdf:type schema:CreativeWork
85 https://doi.org/10.1016/0022-2836(81)90087-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024589839
86 rdf:type schema:CreativeWork
87 https://doi.org/10.1016/j.jcss.2003.04.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021213181
88 rdf:type schema:CreativeWork
89 https://doi.org/10.1016/s0166-218x(03)00382-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023652568
90 rdf:type schema:CreativeWork
91 https://doi.org/10.1093/bioinformatics/18.3.440 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006017712
92 rdf:type schema:CreativeWork
93 https://doi.org/10.1093/nar/28.1.45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004742321
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1142/s0219720004000326 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063004526
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1142/s0219720004000661 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063004556
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1145/640075.640083 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018184175
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1145/974614.974625 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098879727
102 rdf:type schema:CreativeWork
103 https://www.grid.ac/institutes/grid.46078.3d schema:alternateName University of Waterloo
104 schema:name School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
105 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...