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Maurice HT Ling edited this page Jan 20, 2023 · 14 revisions

SWH

Bactome started off as a collection of tools for my study of bacteria, archaebacteria, and complex taxons. Bactome is similar to COPADS in the sense that it is a loose repository of bioinformatics analysis scripts, which may not make sense. Nevertheless, my team and myself develop these tools as and when our project requires. Hopefully, they can be of some use to others.

Manual Pages

  1. Sequence Properties - Analyzing sequences; such as, codon usage bias, amino acid usage bias, peptide properties, etc.
  2. File Splitter - Splitting large text file by number of lines.
  3. GC Percentage Analysis - Analyzing %GC in Genbank record(s).
  4. Genbank File Processor - Generating feature maps of Genbank record(s).
  5. Gene Ontology Over-representation - Analyzing gene list for Gene Ontology over-representation.
  6. OLIgonucleotide Variable Expression Ranker (OLIVER) - Identifying suitable reference (invariant) genes from large transcriptome datasets.
  7. Random Sequence Generator - Generating sets of random sequences.

Licence

Bactome is licensed under GNU General Public Licence version 3 for academic and non-commercial purposes only.

Publications

  1. Liu, TT, Ling, MHT. 2020. BactClass: Simplifying the Use of Machine Learning in Biology and Medicine. Acta Scientific Medical Sciences 4(11): 43-47.
  2. Ling, MHT. 2020. SeqProperties: A Python Command-Line Tool for Basic Sequence Analysis. Acta Scientific Microbiology 3(6): 103-106.
  3. Ling, MHT. 2019. Island: A Simple Forward Simulation Tool for Population Genetics. Acta Scientific Computer Sciences 1(2): 20-22.
  4. Ling MHT. 2018. RANDOMSEQ: Python Command‒line Random Sequence Generator. MOJ Proteomics & Bioinformatics 7(4):206‒208.
  5. Chan, OYW, Keng, BMH, Ling, MHT. 2014. Bactome III: OLIgonucleotide Variable Expression Ranker (OLIVER) 1.0, Tool for Identifying Suitable Reference (Invariant) Genes from Large Microarray Datasets. The Python Papers Source Codes 6: 2.
  6. Chan, OYW, Keng, BMH, Ling, MHT. 2014. Correlation and Variation Based Method for Reference Genes Identification from Large Datasets. Electronic Physician 6(1): 719-727.
  7. Ling, MHT. 2011. Bactome II: Analyzing Gene List for Gene Ontology Over-Representation. The Python Papers Source Codes 3: 3.
  8. Lee, CH, Lee, KC, Oon, JSH, Ling, MHT. 2010. Bactome, I: Python in DNA Fingerprinting. In: Peer-Reviewed Articles from PyCon Asia-Pacific 2010. The Python Papers 5(3):6.

Studies using Bactome

  1. Neo, CY, Ling, MHT. 2020. Prevalence and Length of Open Reading Frames Vary Across Randomly Generated Sequences of Different Nucleotide Compositions. EC Microbiology 16(7): 72-78.
  2. Ardhanari-Shanmugam, KD, Shahrukh, K, B, V, Woo, JH, Thong-Ek, C, Usman, S, Kwek, BZN, Chua, JW, Ling, MHT. 2019. De Novo Origination of Bacillus subtilis 168 Promoters from Random Sequences. Acta Scientific Microbiology 2(11): 07-10.
  3. Kwek, BZN, Ardhanari-Shanmugam, KD, Woo, JH, Usman, S, Chua, JW, B, V, Shahrukh, K, Thong-Ek, C, Ling, MHT. 2019. Random Sequences May Have Putative Beta-Lactamase Properties. Acta Scientific Medical Sciences 3(7): 113-117.
  4. Thong-Ek, C, Usman, S, Woo, JH, Chua, JW, Kwek, BZN, Ardhanari-Shanmugam, KD, B, V, Shahrukh, K, Ling, MHT. 2019. Potential De Novo Origins of Archaebacterial Glycerol-1-Phosphate Dehydrogenase (G1PDH). Acta Scientific Microbiology 2(6): 106-110.
  5. Maitra, A, Ling, MHT. 2019. Codon Usage Bias and Peptide Properties of Pseudomonas balearica DSM 6083T. MOJ Proteomics & Bioinformatics 8(2):27‒39.
  6. Kim, JH, Ling, MHT. 2019. Proteome Diversities Among 19 Archaebacterial Species. Acta Scientific Microbiology 2(5): 20-27.
  7. Ling, MHT. 2019. De Novo Putative Protein Domains from Random Peptides. Acta Scientific Microbiology 2(4): 109-112.
  8. Lee, CH, Oon, JSH, Lee, KC, Lee, CH, Ling, MHT. 2012. Escherichia coli ATCC 8739 Adapts to the Presence of Sodium Chloride, Monosodium Glutamate, and Benzoic Acid after Extended Culture. ISRN Microbiology 2012, Article ID 965356.
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