Lab page @ http://graveleylab.cam.uchc.edu/Graveley/index.html

It takes FASTA DNA sequence as input, and write GFF3 as output. It uses the new NHMMER tool that comes with HMMER 3.1 for HMM searching in RNA:DNA style. NHMMER binaries for 64-bit Linux and Mac OS X are included and will be auto-detected. Multithreading is supported and one can expect roughly linear speed-ups with more CPUs. 

Address of the bookmark: https://github.com/tseemann/barrnap

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.

In the mid 1960's scientists discovered that many genomes contain stretches of highly repetitive DNA sequences ( see Reassociation Kinetics Experiments, and C-Value Paradox ). These sequences were later characterized and placed into five categories:

Simple Repeats - Duplications of simple sets of DNA bases (typically 1-5bp) such as A, CA, CGG etc.
Tandem Repeats - Typically found at the centromeres and telomeres of chromosomes these are duplications of more complex 100-200 base sequences.
Segmental Duplications - Large blocks of 10-300 kilobases which are that have been copied to another region of the genome.
Interspersed Repeats
Processed Pseudogenes, Retrotranscripts, SINES - Non-functional copies of RNA genes which have been reintegrated into the genome with the assitance of a reverse transcriptase.
DNA Transposons
Retrovirus Retrotransposons
Non-Retrovirus Retrotransposons ( LINES )

Currently up to 50% of the human genome is repetitive in nature and as improvements are made in detection methods this number is expected to increase.

On the other hand; In genetics, the term palindrome refers to a sequence of nucleotides along a DNA (deoxyribonucleic acid) or RNA (ribonucleic acid) strand that contains the same series of nitrogenous bases regardless from which direction the strand is analyzed. Akin to a language palindrome—wherein a word or phrase is spelled the same left-to-right as right-to-left (e.g., the word RADAR or the phrase "able was I ere I saw elba")—with genetic palindromes it does not matter whether the nucleic acid strand is read starting from the 3' (three prime) end or the 5' (five prime) end of the strand.

Recent research on palindromes centers on understanding palindrome formation during gene amplification. Other studies have attempted to relate palindrome formation to molecular mechanisms involved in double stranded breaks and in the formation of inverted repeats. Assisted by high speed computers, other groups of scientists link palindrome formation to the conservation of genetic information.

Related to the direction of transcription by RNA polymerase, DNA strands have upstream and downstream terminus defined by differing chemical groups at each end. The ends of each strand of DNA or RNA are termed the 5' (phosphate bound to the 5' position carbon) and 3' (phosphate bound to the 3' carbon) ends to indicate a polarity within the molecule. Using the letters A, T, C, G, to represent the nitrogenous bases adenine, thymine, cytosine, and guanine found in DNA, and the letters A, U, C, G to represent the nitrogenous bases adenine, uracil, cytosine, guanine found in RNA (Note that uracil in RNA replaces the thymine found in DNA), geneticists usually represent DNA by a series of base codes (e.g., 5' AATCGGATTGCA 3'). The base codes are usually arranged from the 5' end to the 3' end.

Because of specific base pairing in DNA (i.e., adenine (A) always bonds with (thymine (T) and cytosine (C) always bonds with guanine (G)) the complimentary stand to the sequence 5' AATCGGATTGCA 3' would be 3' TTAGCCTAACGT 5'.

With palindromes the sequences on the complimentary strands read the same in either direction. For example, a sequence of 5' GAATTC3' on one strand would be complimented by a 3' CTTAAG 5' strand. In either case, when either strand is read from the 5' prime end the sequence is GAATTC. Another example of a palindrome would be the sequence 5' CGAAGC 3' that, when reversed, still reads CGAAGC.

Palindromes are important sequences within nucleic acids. Often they are the site of binding for specific enzymes (e.g., restriction endobucleases) designed to cut the DNA strands at specific locations (i.e., at palindromes).

Palindromes may arise from brakeage and chromosomal inversions that form inverted repeats that compliment each other. When a palindrome results from an inversion, it is often referred to as an inverted repeat. For example, the sequence 5' CGAAGC 3', if inverted (reversed 180°), still reads CGAAGC.

The European Molecular Biology Open Software Suite (EMBOSS) includes some basic tools for finding tandem repeats and inverted repeats (see B.6.22. Applications in group Nucleic:repeats). There are many on-line services providing the EMBOSS tools, for example:

• Wageningen Bioinformatics Webportal EMBOSS explorer
• Mobyle@Pasteur
• Soaplab2 Web Services at Vital-IT

For more sophisticated repeat finding you will want to look at tools using Repbase for example:

• CENSOR
  • CENSOR@GIRI
  • CENSOR@EMBL-EBI
• RepeatMasker
• MUMmer (scan_for_match)
• Emboss Palindrome

Other nucleotide repeat finding methods found by a couple of web searches:

• Tandem Repeats Finder
• RECON
• RepeatRunner
• REPuter
• Imperfect Microsatellite Extractor (IMEx)
• Spectral Repeat Finder (SRF)
• REPFIND
• CRISPRfinder
• GrailEXP
• CONREPP
• find_palindromes
• Palindrome
• EMBOSS Palindrome
• Palindrome Search

GenBank release 257.0 (8/15/2023) is now available on the NCBI FTP site. This release has 25.10 trillion bases and 3.69 billion records.

The current release has:

• 246,119,175 traditional records containing 2,112,058,517,945 base pairs of sequence data
• 2,631,493,489 WGS records containing 22,294,446,104,543 base pairs of sequence data
• 686,271,945 bulk-oriented TSA records containing 646,176,166,908 base pairs of sequence data
• 124,421,006 bulk-oriented TLS records containing 48,289,699,026 base pairs of sequence data

http://www.putonti-lab.com/uploads/4/5/3/0/45307835/s-plot2_tutorial.pdf

http://journals.sagepub.com/doi/pdf/10.1177/1176934318797354

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot

The only input requierement for UPhO is a tree (or trees) in Newick format in which the leaves are named with a species idenfifier, a field separator, and sequence identifier. By default, the field separator is the character "|" but custom delimiters can be defined. Examples of trees to test UPhO are provided in the TestData folder.

Address of the bookmark: https://github.com/ballesterus/UPhO

See the new IgBLAST manual on the NCBI GitHub site for more information on setting up and running IgBLAST.

If you have any questions or concerns, please email us at blast-help@ncbi.nlm.nih.gov

Address of the bookmark: https://bioconductor.org/packages/release/bioc/html/doubletrouble.html