A new website build [...]]]> Updating to the WordPress 3.0, everything is fine.

A new website build for SNP.

http://nii.paulyu.info

I use Mosaik to align Natto to B. Cereus. Hope it will [...]]]> I compare two genomes ( Nattō and Bacillus cereus). Here is a new format, fastq ref, I learned.

I use Mosaik to align Natto to B. Cereus. Hope it will work.

Yanmei Shi, et al., Nature

In the past weeks, the researcher use sequencing technique to reveal the environmental species’ information such as richness and functionality. The problem is not all the DNA sequences will be translated to the functional proteins. Some of the proteins might not [...]]]> Metatranscriptomics reveals unique microbial small RNAs in the ocean’s water column

Yanmei Shi, et al., Nature

In the past weeks, the researcher use sequencing technique to reveal the environmental species’ information such as richness and functionality. The problem is not all the DNA sequences will be translated to the functional proteins. Some of the proteins might not to be the function under some situation, such as lack of enzyme, incorrect post-translation modification and so on. The information we cannot obtain directly from DNA sequence. Therefore, metaproteomics and metatranscriptomics are proposed to conquer these problems.

They perform RNA sequencing directly from natural microbial assemblages. Some previous studies report that they found some of the complementary DNA can’t find significant homology against with the current database. This indicates that some of the cDNA sequences might transcribe from uncharacterized proteins. The authors used four various data sets from the same location but distinct depth at Hawaii Ocean and found a large fraction of cDNA sequences can’t to be found in the coding region or even in ribosomal RNAs using homology search. That plentiful portion of cDNA sequences is classified as small RNAs and putative sRNAs..The sRNAs are a short piece of RNA, but it will not translate to protein, and usually we can be found then in the intergenic regions on the genomes.

They use a covariance-model-based algorithm to search all unassigned cDNA reads against with known sequence and structural similarity in Rfam. Then, the self-clustering approach is applied to all cDNA reads for better characterization of sRNA. Among found 66 groups, 9 groups were identified sRNA families in Rfam database and the rest groups were psRNA groups mapped to IGRs metagenomics fragments derived from marine planktonic microorganisms.

Finally, sRNAs are claimed to be the factor for controlling the gene expression in response to variable environmental conditions. Also, it is the first time they have some information about the correlation between metatranscriptomics and environment in RNA aspect. This method can be a good sensor for environment changing.

I have a little missing in this paper. They report so much biology information that I can’t fully understand. However, I think the address a very important issue that the environment absolutely can change the protein expression by the certain biology mechanisms.

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Nathan C Verberkmoes, et al., International Society for Microbial Ecology

This paper describes a newly approach for metagenomics data analysis, called metaproteomics. They take two individual samples from a pair of twins who have gastroenteritis. One is treated with anti-inflammatory drugs and the other one don’t have any treatment. [...]]]> Shotgun metaproteomics of the human distal gut microbiota

Nathan C Verberkmoes, et al., International Society for Microbial Ecology

This paper describes a newly approach for metagenomics data analysis, called metaproteomics. They take two individual samples from a pair of twins who have gastroenteritis. One is treated with anti-inflammatory drugs and the other one don’t have any treatment. The fecal samples were collected for this research study.

The microbial cells were extracted from fecal samples by using differential centrifugation. Each sample has a technical replicate to test the reproducibility of this approach. Then the LC-MS/MS is performed for the high-throughput data acquisition. The number of identify peptides are around 2,000 and 3,000 for sample 7 and 8 respectively against two human subject metagenome database. There are other three databases, which contain normal gut microbiota, human contamination and so on.

Those protein identification results are mapped to the COG database. The results show highly reproducible and consistent in COG categories, and several COG categories were more represented in the average microbial metagenomes which reported by previous study in the present study. The majority of detected proteins were involved in translation, carbohydrate metabolism or energy production.

The protein quantification results were derived from the normalized spectral abundance factors (NSAF) across all samples. The most abundance proteins are elastase, chymotrypsin C and salivary amylases, which are expected in the human gut microbial communality.

Those proteins which cannot be identified against with the databases are classified as hypothetical proteins. This class of proteins may belong to novel protein families, which are not being fully study and characterized by previous research. The majority of these proteins are highly representing in the human gut only.

This work is the first large-scale metaproteomics research. They take advantages of shotgun sequencing techniques to discovery new knowledge from a different aspect of view. Now the research can have information from before and after protein translation. For protein quantification, if the internal standard is used while data collecting stage, it will be more accurate in protein abundance estimation, and it is still a problem to detect those low detectable and abundance peptides.

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