Limits...
Medicago truncatula transporter database: a comprehensive database resource for M. truncatula transporters.

Miao Z, Li D, Zhang Z, Dong J, Su Z, Wang T - BMC Genomics (2012)

Bottom Line: Although studies have effectively characterized individual M. truncatula transporters in several databases, until now there has been no available systematic database that includes all transporters in M. truncatula.A chromosome distribution map and text-based Basic Local Alignment Search Tools were also created.A user-friendly web interface and regular updates make MTDB valuable to researchers in related fields.

View Article: PubMed Central - HTML - PubMed

Affiliation: State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.

ABSTRACT

Background: Medicago truncatula has been chosen as a model species for genomic studies. It is closely related to an important legume, alfalfa. Transporters are a large group of membrane-spanning proteins. They deliver essential nutrients, eject waste products, and assist the cell in sensing environmental conditions by forming a complex system of pumps and channels. Although studies have effectively characterized individual M. truncatula transporters in several databases, until now there has been no available systematic database that includes all transporters in M. truncatula.

Description: The M. truncatula transporter database (MTDB) contains comprehensive information on the transporters in M. truncatula. Based on the TransportTP method, we have presented a novel prediction pipeline. A total of 3,665 putative transporters have been annotated based on International Medicago Genome Annotated Group (IMGAG) V3.5 V3 and the M. truncatula Gene Index (MTGI) V10.0 releases and assigned to 162 families according to the transporter classification system. These families were further classified into seven types according to their transport mode and energy coupling mechanism. Extensive annotations referring to each protein were generated, including basic protein function, expressed sequence tag (EST) mapping, genome locus, three-dimensional template prediction, transmembrane segment, and domain annotation. A chromosome distribution map and text-based Basic Local Alignment Search Tools were also created. In addition, we have provided a way to explore the expression of putative M. truncatula transporter genes under stress treatments.

Conclusions: In summary, the MTDB enables the exploration and comparative analysis of putative transporters in M. truncatula. A user-friendly web interface and regular updates make MTDB valuable to researchers in related fields. The MTDB is freely available now to all users at http://bioinformatics.cau.edu.cn/MtTransporter/.

Show MeSH
Computational prediction to identify Medicago truncatula transporters. We used Basic Local Alignment Search Tool (BLAST) and HMMER searches in computational predictions to identify M. truncatula transporters. First, we used the transport protein sequences of Arabidopsis thaliana and the Transporter Classification Database (TCDB) to conduct a BLAST search with M. truncatula protein sequences, provided by the International Medicago Genome Annotated Group (IMGAG). The preprocess data were then analyzed for the presence of a potential transmembrane domain (TMD) using two algorithms: TMHMM and HMMTOP 2.0. Afterward, we used the annotated sequences to conduct a HMMER search with the Pfam annotations from the Pfam database, version 24.0. In addition, all original transporters were compared with proteins of the M. truncatula transporter annotation at the Noble Foundation. We used Perl scripts to analyze the results.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3298476&req=5

Figure 1: Computational prediction to identify Medicago truncatula transporters. We used Basic Local Alignment Search Tool (BLAST) and HMMER searches in computational predictions to identify M. truncatula transporters. First, we used the transport protein sequences of Arabidopsis thaliana and the Transporter Classification Database (TCDB) to conduct a BLAST search with M. truncatula protein sequences, provided by the International Medicago Genome Annotated Group (IMGAG). The preprocess data were then analyzed for the presence of a potential transmembrane domain (TMD) using two algorithms: TMHMM and HMMTOP 2.0. Afterward, we used the annotated sequences to conduct a HMMER search with the Pfam annotations from the Pfam database, version 24.0. In addition, all original transporters were compared with proteins of the M. truncatula transporter annotation at the Noble Foundation. We used Perl scripts to analyze the results.

Mentions: In MTDB, we used Basic Local Alignment Search Tool (BLAST)[21] and HMMER [22] searches in computational predictions to identify putative M. truncatula transporters [Figure 1]. First, we respectively used 1,278 transport protein sequences of A. thaliana from TransportDB and 6,080 transporters in TCDB to conduct a BLASTp search with 47,529 M. truncatula protein sequences provided by the IMGAG. Of the 6,080 transporters in TCDB, 248 were transport proteins of A. thaliana, of which 181 were also found in TransportDB. We set the e-value cut-off at 0.0001 and identity at > 30% when we used Perl http://www.perl.org and BioPerl [23] scripts to analyze the BLASTp search results. A total of 5,706 (12.0%) M. truncatula proteins were predicted by at least one procedure, of which 1,974 were identified by two procedures. We selected only the top five homologs for easily storage. The 47,529 M. truncatula proteins were then predicted from the genome sequence (IMGAG sequence release version 3.5v3) and analyzed for the presence of a potential transmembrane domain (TMD) using two algorithms: TMHMM [24] and HMMTOP 2.0[25]. Of the IMGAG-annotated proteins, 17,471 (36.8%) were predicted by one or more programs to contain at least one TMD, of which 8,889 were identified by the two programs. In addition, we used the annotated sequences to conduct a HMMER search with the Pfam annotations that came from the Pfam database, version 24.0. We used Perl scripts to analyze the HMMER search result to obtain all annotated sequences whose pfamID were contained by the TCDB transporters and A. thaliana pfamID sets. In the end, 3,598 (7.5%) putative transport proteins that contained at least one TMD were annotated; they had sequence homology with proteins in TCDB and A. thaliana transporters. We also used SOSUI [26] web-based software to re-predict the protein transmembrane segment. Of the 3,598 putative transporters, 2,780 were predicted to contain at least one TMD (77.3%). Benedito et al. published a comprehensive analysis of M. truncatula transporters [8]. We compared our analysis with Benedito et al.'s published results, which were based on the IMGAG V2.0 (2,582). We mapped between transporters based on IMGAG V3.5v3 and IMGAG V2. Of the 3,598 putative transporters, 2,507 were assigned to 2,047 published transporters; the overlap rate was 79.3% and the validated rate was 69.7%. In addition, all 3,598 proteins were compared with proteins of the Medicago transporter annotation obtained from the Noble Foundation based on the latest IMGAG V3.5 V3. Of the 3,598 predicted transport proteins, 2,622 (72.9%) were also found in the Medicago transporter annotation at the Noble Foundation. Furthermore, we searched the annotation of the transporters based on IMGAG V3.5v3 occurring in the bioinformatics lab at the Noble Foundation but absent in our predictions using the keywords "transporter" and "transport." Finally, an additional 67 proteins were predicted.


Medicago truncatula transporter database: a comprehensive database resource for M. truncatula transporters.

Miao Z, Li D, Zhang Z, Dong J, Su Z, Wang T - BMC Genomics (2012)

Computational prediction to identify Medicago truncatula transporters. We used Basic Local Alignment Search Tool (BLAST) and HMMER searches in computational predictions to identify M. truncatula transporters. First, we used the transport protein sequences of Arabidopsis thaliana and the Transporter Classification Database (TCDB) to conduct a BLAST search with M. truncatula protein sequences, provided by the International Medicago Genome Annotated Group (IMGAG). The preprocess data were then analyzed for the presence of a potential transmembrane domain (TMD) using two algorithms: TMHMM and HMMTOP 2.0. Afterward, we used the annotated sequences to conduct a HMMER search with the Pfam annotations from the Pfam database, version 24.0. In addition, all original transporters were compared with proteins of the M. truncatula transporter annotation at the Noble Foundation. We used Perl scripts to analyze the results.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3298476&req=5

Figure 1: Computational prediction to identify Medicago truncatula transporters. We used Basic Local Alignment Search Tool (BLAST) and HMMER searches in computational predictions to identify M. truncatula transporters. First, we used the transport protein sequences of Arabidopsis thaliana and the Transporter Classification Database (TCDB) to conduct a BLAST search with M. truncatula protein sequences, provided by the International Medicago Genome Annotated Group (IMGAG). The preprocess data were then analyzed for the presence of a potential transmembrane domain (TMD) using two algorithms: TMHMM and HMMTOP 2.0. Afterward, we used the annotated sequences to conduct a HMMER search with the Pfam annotations from the Pfam database, version 24.0. In addition, all original transporters were compared with proteins of the M. truncatula transporter annotation at the Noble Foundation. We used Perl scripts to analyze the results.
Mentions: In MTDB, we used Basic Local Alignment Search Tool (BLAST)[21] and HMMER [22] searches in computational predictions to identify putative M. truncatula transporters [Figure 1]. First, we respectively used 1,278 transport protein sequences of A. thaliana from TransportDB and 6,080 transporters in TCDB to conduct a BLASTp search with 47,529 M. truncatula protein sequences provided by the IMGAG. Of the 6,080 transporters in TCDB, 248 were transport proteins of A. thaliana, of which 181 were also found in TransportDB. We set the e-value cut-off at 0.0001 and identity at > 30% when we used Perl http://www.perl.org and BioPerl [23] scripts to analyze the BLASTp search results. A total of 5,706 (12.0%) M. truncatula proteins were predicted by at least one procedure, of which 1,974 were identified by two procedures. We selected only the top five homologs for easily storage. The 47,529 M. truncatula proteins were then predicted from the genome sequence (IMGAG sequence release version 3.5v3) and analyzed for the presence of a potential transmembrane domain (TMD) using two algorithms: TMHMM [24] and HMMTOP 2.0[25]. Of the IMGAG-annotated proteins, 17,471 (36.8%) were predicted by one or more programs to contain at least one TMD, of which 8,889 were identified by the two programs. In addition, we used the annotated sequences to conduct a HMMER search with the Pfam annotations that came from the Pfam database, version 24.0. We used Perl scripts to analyze the HMMER search result to obtain all annotated sequences whose pfamID were contained by the TCDB transporters and A. thaliana pfamID sets. In the end, 3,598 (7.5%) putative transport proteins that contained at least one TMD were annotated; they had sequence homology with proteins in TCDB and A. thaliana transporters. We also used SOSUI [26] web-based software to re-predict the protein transmembrane segment. Of the 3,598 putative transporters, 2,780 were predicted to contain at least one TMD (77.3%). Benedito et al. published a comprehensive analysis of M. truncatula transporters [8]. We compared our analysis with Benedito et al.'s published results, which were based on the IMGAG V2.0 (2,582). We mapped between transporters based on IMGAG V3.5v3 and IMGAG V2. Of the 3,598 putative transporters, 2,507 were assigned to 2,047 published transporters; the overlap rate was 79.3% and the validated rate was 69.7%. In addition, all 3,598 proteins were compared with proteins of the Medicago transporter annotation obtained from the Noble Foundation based on the latest IMGAG V3.5 V3. Of the 3,598 predicted transport proteins, 2,622 (72.9%) were also found in the Medicago transporter annotation at the Noble Foundation. Furthermore, we searched the annotation of the transporters based on IMGAG V3.5v3 occurring in the bioinformatics lab at the Noble Foundation but absent in our predictions using the keywords "transporter" and "transport." Finally, an additional 67 proteins were predicted.

Bottom Line: Although studies have effectively characterized individual M. truncatula transporters in several databases, until now there has been no available systematic database that includes all transporters in M. truncatula.A chromosome distribution map and text-based Basic Local Alignment Search Tools were also created.A user-friendly web interface and regular updates make MTDB valuable to researchers in related fields.

View Article: PubMed Central - HTML - PubMed

Affiliation: State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.

ABSTRACT

Background: Medicago truncatula has been chosen as a model species for genomic studies. It is closely related to an important legume, alfalfa. Transporters are a large group of membrane-spanning proteins. They deliver essential nutrients, eject waste products, and assist the cell in sensing environmental conditions by forming a complex system of pumps and channels. Although studies have effectively characterized individual M. truncatula transporters in several databases, until now there has been no available systematic database that includes all transporters in M. truncatula.

Description: The M. truncatula transporter database (MTDB) contains comprehensive information on the transporters in M. truncatula. Based on the TransportTP method, we have presented a novel prediction pipeline. A total of 3,665 putative transporters have been annotated based on International Medicago Genome Annotated Group (IMGAG) V3.5 V3 and the M. truncatula Gene Index (MTGI) V10.0 releases and assigned to 162 families according to the transporter classification system. These families were further classified into seven types according to their transport mode and energy coupling mechanism. Extensive annotations referring to each protein were generated, including basic protein function, expressed sequence tag (EST) mapping, genome locus, three-dimensional template prediction, transmembrane segment, and domain annotation. A chromosome distribution map and text-based Basic Local Alignment Search Tools were also created. In addition, we have provided a way to explore the expression of putative M. truncatula transporter genes under stress treatments.

Conclusions: In summary, the MTDB enables the exploration and comparative analysis of putative transporters in M. truncatula. A user-friendly web interface and regular updates make MTDB valuable to researchers in related fields. The MTDB is freely available now to all users at http://bioinformatics.cau.edu.cn/MtTransporter/.

Show MeSH