| Chapter title |
Gene Calling and Bacterial Genome Annotation with BG7.
|
|---|---|
| Chapter number | 12 |
| Book title |
Bacterial Pangenomics
|
| Published in |
Methods in molecular biology, October 2014
|
| DOI | 10.1007/978-1-4939-1720-4_12 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-1719-8, 978-1-4939-1720-4
|
| Authors |
Tobes R, Pareja-Tobes P, Manrique M, Pareja-Tobes E, Kovach E, Alekhin A, Pareja E, Tobes, Raquel, Pareja-Tobes, Pablo, Manrique, Marina, Pareja-Tobes, Eduardo, Kovach, Evdokim, Alekhin, Alexey, Pareja, Eduardo, Raquel Tobes, Pablo Pareja-Tobes, Marina Manrique, Eduardo Pareja-Tobes, Evdokim Kovach, Alexey Alekhin, Eduardo Pareja |
| Abstract |
New massive sequencing technologies are providing many bacterial genome sequences from diverse taxa but a refined annotation of these genomes is crucial for obtaining scientific findings and new knowledge. Thus, bacterial genome annotation has emerged as a key point to investigate in bacteria. Any efficient tool designed specifically to annotate bacterial genomes sequenced with massively parallel technologies has to consider the specific features of bacterial genomes (absence of introns and scarcity of nonprotein-coding sequence) and of next-generation sequencing (NGS) technologies (presence of errors and not perfectly assembled genomes). These features make it convenient to focus on coding regions and, hence, on protein sequences that are the elements directly related with biological functions.In this chapter we describe how to annotate bacterial genomes with BG7, an open-source tool based on a protein-centered gene calling/annotation paradigm. BG7 is specifically designed for the annotation of bacterial genomes sequenced with NGS. This tool is sequence error tolerant maintaining their capabilities for the annotation of highly fragmented genomes or for annotating mixed sequences coming from several genomes (as those obtained through metagenomics samples). BG7 has been designed with scalability as a requirement, with a computing infrastructure completely based on cloud computing (Amazon Web Services). |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 8% |
| United States | 1 | 4% |
| Belgium | 1 | 4% |
| Russia | 1 | 4% |
| Unknown | 20 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 32% |
| Student > Ph. D. Student | 6 | 24% |
| Student > Master | 3 | 12% |
| Student > Bachelor | 2 | 8% |
| Professor | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 1 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 52% |
| Biochemistry, Genetics and Molecular Biology | 4 | 16% |
| Chemical Engineering | 1 | 4% |
| Veterinary Science and Veterinary Medicine | 1 | 4% |
| Mathematics | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 2 | 8% |