unzip blatSrc35.zip

to unpack the archive. This gives a directory blatSrc now move into that directory.

#cd blatSrc

before you begin read the README file that comes with the source code.

One thing about building blat is that you need to set the MACHTYPE variable so that the BLAT sources know what type of machine you are compiling the software on.

on most *nix machines, typing

echo $MACHTYPE

will return the machine architecture type.

On my CentOS 6 based system this gave:

x86_64-redhat-linux-gnu

However, what BLAT requires is the 'short value' (ie the first part of the MACHTYPE). To correct this, in the bash shell type (change this to the correct MACHTYPE for your system)

MACHTYPE=x86_64
export MACHTYPE

now running the command:

echo $MACHTYPE

should give the correct short form of the MACHTYPE:

x86_64

now create the directory lib/$MACHTYPE in the source tree. ie:

mkdir lib/$MACHTYPE

For my machine, lib/x86_64 already existed, so I did not have to do this, but this is not the case for all architectures.

The BLAT code assumes that you are compiling BLAT as a non-privileged (ie non-root) user. As a result, you must create the directory for the executables to go into:

mkdir ~/bin/$MACHTYPE

If you are installing as a normal user, edit your .bashrc to add the following (change the x86_64 to be your MACHTYPE):

export PATH=~/bin/x86_64::$PATH

For me, though, this was not good enough. I wanted the executables in /usr/local/bin where all my other code goes. As a result I did some hackery...

There is a master make template in the inc directory called common.mk and I edited this file with the command:

vi inc/common.mk

I replaced the line

    BINDIR=${HOME}/bin/${MACHTYPE}

with

    BINDIR=/usr/local/bin

saved and quit (as this is in my path, I do not need to do anything else)

All the preparation is now done and you can create the blat executables by going into the toplevel of the blat source tree (for me it was /usr/local/src/blat/blatSrc, but change to wherever you unpacked blat into).

Now simply run the command:

make

to compile the code.

Blat installed cleanly and the executables were all neatly placed in /usr/local/bin/x86_64, just like I wanted.

now simply running the command:

blat

on the command line gives me information on blat and sample usage.

Blat is installed and it's installed properly in my system code tree!!!

Align sequences and output the alignment in MSF format:

kalign -i BB11001.tfa -f msf  -o out.msf

Align sequences and output the alignment in clustal format:

kalign -i BB11001.tfa -f clu -o out.clu

Re-align sequences in an existing alignment:

kalign -i BB11001.msf  -o out.afa

Reformat existing alignment:

kalign -i BB11001.msf -r afa -o out.afa

Address of the bookmark: https://github.com/TimoLassmann/kalign

When the query file format is fasta, you can specify many threads to process it. It can reduce run time linearly, and use almost equal memory as the original lastz program. This is useful when you lastz a big query file to a huge reference like human whole genome sequence.

The program is an extension on the original lastz program which was written by Bob Harris (the LASTZ guy).

Address of the bookmark: https://github.com/jnarayan81/parallelLastz

Address of the bookmark: https://github.com/AdamCicherski/AlfaPang

Key Components

1. Sample Collection: Meta-transcriptomics begins with the collection of environmental samples. These samples are often complex, containing a wide range of microorganisms.

2. RNA Extraction: RNA is extracted from the sample, which includes mRNA, rRNA, tRNA, and other non-coding RNAs. This step is crucial as it determines the quality and representativeness of the data.

3. Sequencing: High-throughput RNA sequencing (RNA-seq) technologies are used to obtain sequences of the RNA transcripts. This step provides a vast amount of data on the RNA molecules present in the sample.

4. Data Analysis: Computational tools and bioinformatics methods are employed to process and analyze the sequencing data. This involves mapping RNA sequences to reference genomes or transcriptomes, identifying expressed genes, and quantifying their abundance.

5. Functional Annotation: The functional roles of identified transcripts are inferred based on known gene functions, allowing researchers to understand the metabolic and ecological functions of the microbial community.

Applications

1. Environmental Monitoring: Meta-transcriptomics can be used to monitor the health and functional status of ecosystems. For example, it can help assess the impact of pollution on microbial communities by revealing changes in gene expression related to stress response and degradation processes.

2. Microbiome Research: In human health, meta-transcriptomics offers insights into the gut microbiome’s functional state. It helps in understanding how microbial communities interact with their host, how they respond to dietary changes, and their role in health and disease.

3. Biotechnology: The technique can aid in the discovery of novel enzymes and bioactive compounds by profiling microbial communities in extreme environments or industrial processes.

4. Disease Pathogenesis: By analyzing RNA profiles from disease-associated environments, researchers can uncover pathogen-host interactions and identify potential targets for therapeutic interventions.

Challenges

1. Complexity of Data: The sheer volume and complexity of data generated by meta-transcriptomics can be overwhelming. Effective data management and advanced computational tools are required to extract meaningful insights.

2. Sampling Bias: Environmental samples can be heterogeneous, and RNA extraction methods may introduce biases, potentially affecting the accuracy of the results.

3. Reference Databases: Incomplete or biased reference databases can hinder the accurate functional annotation of transcripts, especially when studying novel or poorly characterized organisms.

Future Directions

Meta-transcriptomics is a rapidly evolving field, with ongoing advancements in sequencing technologies and bioinformatics. Future research may focus on improving data integration, developing more comprehensive reference databases, and enhancing our understanding of microbial community dynamics in various environments. As these challenges are addressed, meta-transcriptomics will continue to provide valuable insights into the functional roles of microorganisms and their interactions within ecosystems.

Conclusion

Meta-transcriptomics represents a powerful tool for exploring the functional aspects of microbial communities in their natural environments. By capturing a snapshot of gene expression and metabolic activities, this approach offers a deeper understanding of ecological interactions, health implications, and biotechnological potentials. As technology and methodologies advance, meta-transcriptomics is poised to make significant contributions to our knowledge of the microbial world.

More at https://bitbucket.org/henryhzhang/rcircos/src

Address of the bookmark: https://bitbucket.org/henryhzhang/rcircos/src