biology discussion


Article 1: Tu, Q., He, Z., & Zhou, J. (2014). Strain/species identification in metagenomes using genome-specific markers. Nucleic acids research, 42(8), e67-e67.

  • what are the main findings? 

In this research, accurate identification of microorganism at species/strain level were the main focus and the following were the main findings:

  1. identified genome-specific markers are specific to their targeting genomes.
  2. Sensitivity evaluation against synthetic metagenomes with different coverage indicate that 50 genome-specific markers per strain are sufficient to identify microbial strains having greater than 0.25X coverage.
  • From a gastrointestinal tract metagenome, application of genome-specific markers identified 45 and 74 microbial strains/species associated with type 2 diabetes patients and obese/lean individuals respectively.
  • what materials and methods they used? 

Materials included reference genome sequences downloaded from human microbiome project data analysis and coordination center (HMPDACC) and NCBI GenBankDatabases, downloaded human genome sequences and downloaded duplicated genome sequences. Also, four mock community metagenomes made of 21 bacterial strains were obtained from NCBI and recently sequenced microbial genomes.

The methods used for analysis was the MEGABLAST program and t-test was applied for evaluating statistical significance of T2D-associated microbial strains/species.Also, response ratio analysis was used for illustrating obesity-associated microbial strain/species while Benjamini-Hochberg false discovery rate (FDR) analysis was used for detecting microbial strains with greater than 5 normalized reads.

  • how does it fit in the general picture of the environmental or medical microbiome research?

The research fits in the general picture of the medical microbiome in that it makes it possible for metagenomics profiling of T2D using the associated microbial strains/species. In particular, to evaluate whether a selected genome-specific marker can be applied in the identification of a disease-associated microbial strains/species in the human body.

Also, the research helps in metagenomics profiling of obesity-associated microbial strains/species. In particular, gut genome specific markers were used to identify obesity associated microbial strain/species form 18 individuals of which 9 were diagnosed as obese and the rest as lean/overweight.

  • how important are the findings to applications in the real world? 

The findings are important in determining the detection limit and true positive calling thresholds for microbial identification using genome specific markers. In particular, knowing the sequence coverage value at which microbial genome is identifiable using genome-specific markers as well as knowing the number of genome-specific markers required for effective identification of microbial strain/species.

Furthermore, the findings are of great importance as it shows that the genome-specific marker’s approach can be used for rapid, direct and accurate identification of microorganisms for strain/species by using the metagenome levels.


Article 2: Shao, W., Zhang, M., Lam, H., & Lau, S. C. (2015). A peptide identification-free, genome sequence-independent shotgun proteomics workflow for strain-level bacterial differentiation. Scientific reports, 5, 14337.

  • what are the main findings? 

A shotgun proteomic method is emerging in the identification and differentiation of bacteria; however, the method is limited by mass spectra of peptides to genome-derived peptide sequences. This study mainly identifies the following:

  1. Using the shotgun Proteomic technic, one can separate bacteria strains ( coli) based on source i.e. omnivore, carnivore, herbivore etc.
  2. A genome sequence-independent method that uses the resolving power and accuracy of mass spectrometry without peptide identification was tested and found working. In particular, it was determined that for coli. from the same group, isolates are generally closer to each other than isolates between groups based on two proteomic fingerprints (UNI and ID) as well as the REP-PCP fingerprints.
  • It was found that similarity-clustering algorithm was effective in generating proteomic fingerprints in bacteria.
  1. Also, it was determined that the tuning of the shotgun proteomic performance fingerprint libraries was possible through the filtering of consensus spectra byubiquity which shows a decrease of the number of consensus spectra with increase in ubiquity.
  2. Similarity clustering workflow was shown to be capable of being used for identification of biomarkers for bacterial identification.
  • what materials and methods they used? 


The material used are E. coli from four different sources i.e from raw sewage, from freshly voided feces from pet dogs, farm pigs and feral cows.


A CHROMagerTMECC was used for isolating E. coli from other Gram-negative bacteria as well as suppressing growth of gram-negative bacteria. A multiplex PCR assays was used for verifying blue colonies while another PCR assays was used for targeting ca. 544bp fragment of the 16S rRNA gene specific for E. coli. The PCR methods are generally DNA fingerprinting based methods.Other methods included the LC-MS/MS analysis technic used for tryptic peptide isolation while the MS/MS spectra analysis was used for similarity clustering and peptide identification. Supplementary methods for isolation used the chromatographic technique.

  • how does it fit in the general picture of the environmental or medical microbiome research? 

The method fit in the general medical microbiome research as it uses similarity-clustering algorithm in search for mass spectra derived from the same peptide and merge them into a unique spectrum in order to generate proteomic fingerprints of bacterial isolates.

  • how important are the findings to applications in the real world? 

The discovery of the method is crucial as it can be used in the field of environmental microbiology, clinical microbiology and applied microbiology. In particular, it is possible to use the shotgun proteomic technic to identify E. coli from different sources based on their biomarker, i.e. omnivore, carnivores, herbivores etc.



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