Address of the bookmark: http://www.phrap.org/consed/consed.html

The "Ifs" of NGS QC and Trimming

1. Ensures Data Integrity
   If you want to minimize errors in downstream analyses, QC and trimming remove low-quality reads and bases, ensuring high-confidence data. This step is essential for reliable variant calling, assembly, and other applications.

2. Removes Contaminants
   If adapter sequences or contaminants are present in the raw reads, trimming can eliminate them. This prevents issues like misalignment or incorrect biological interpretations, ensuring cleaner data for analysis.

3. Improves Mapping and Assembly
   If your goal is better alignment to a reference genome or improved de novo assembly, trimming low-quality bases and adapters is critical. High-quality reads map more efficiently and generate more accurate assemblies.

4. Reduces Computational Load
   If you want to save computational resources, trimming reduces the dataset size, which speeds up processing and analysis. Clean datasets mean less computational time spent on processing low-quality data.

5. Prepares for Standardized Analyses
   If your project involves multiple datasets, QC and trimming ensure uniformity across them. This standardization makes comparisons valid and reproducible, particularly in large collaborative studies.

The "Buts" of NGS QC and Trimming

1. Risk of Over-Trimming
   But excessive trimming can lead to the loss of informative sequences, reducing read depth and potentially discarding biologically relevant data. This is especially critical in studies with limited sequencing depth.

2. Bias Introduction
   But trimming algorithms might introduce biases, especially if they inadvertently remove sequences with specific biological patterns. This can skew results and compromise biological insights.

3. Loss of Context in Paired-End Reads
   But trimming one read in a pair more than the other can lead to loss of pairing information. This complicates downstream analyses that rely on paired-end data, such as structural variant detection.

4. Time and Resource Intensive
   But running QC and trimming for large datasets can be computationally expensive and time-consuming. As sequencing depth increases, preprocessing becomes a bottleneck in the analysis pipeline.

5. Variable Standards
   But the criteria for trimming (e.g., quality threshold, minimum read length) can vary between tools and datasets. This variability may affect reproducibility and comparability of results across studies.

Balancing the "Ifs" and "Buts"

To maximize the benefits of QC and trimming while mitigating the challenges, consider the following best practices:

• Use QC Tools Wisely: Start with tools like FastQC to identify quality issues in your raw data. Visualizing quality metrics helps tailor your trimming parameters.

• Choose Reliable Trimming Tools: Tools like Trimmomatic, Cutadapt, and BBduk offer adaptive and customizable trimming options. Select one that aligns with your dataset and project goals.

• Set Reasonable Parameters: Avoid over-trimming by setting quality thresholds and minimum read lengths that balance data retention and quality improvement.

• Test Downstream Effects: Validate the impact of QC and trimming on downstream analyses, such as alignment efficiency, variant calling accuracy, or assembly quality.

• Document Your Workflow: Maintain detailed records of the parameters and tools used for QC and trimming. This ensures reproducibility and enables better troubleshooting.

Conclusion

NGS quality control and trimming are essential steps to ensure reliable and accurate data for analysis. While the "ifs" highlight the clear benefits of these steps, the "buts" remind us of the potential pitfalls. By adopting best practices and carefully balancing these considerations, you can optimize your preprocessing workflow and unlock the full potential of your sequencing data.

Address of the bookmark: https://github.com/soedinglab/metaeuk

http://www.gigasciencejournal.com/content/2/1/10/abstract

http://blogs.nature.com/news/2013/07/genome-assembly-contest-prompts-soul-searching.html

http://assemblathon.org/post/44431915644/feedback-and-analysis-of-the-assemblathon-2-p

H. contortus is a member of the superfamily trichostrongyloidea (Strongylida) which contains most of the economically important parasitic nematodes of grazing livestock. These parasites cost the global livestock industry billions of dollars per annum in lost production and drug costs. A common type of clover may be a preventative or palliative for the disease. However, some particular breeds of sheep, such as the Gulf Coast Native from the Southern United States, have been shown to have developed special resistance to H. contortus.

Getting the full genome can help to tackle the problem and understand the resistance mechanism with an ease. Moreover, the genome could now provide a comprehensive understanding of how treatments against parasitic worms work and point to further new treatments and vaccines. By comparing the genome of the barber's pole worm with those of worms that have acquired drug resistance, researchers expect to reveal information about how and why resistance has occurred. Till now, researchers have uncovered essential information in the fight against drug resistance in worms.

Reference:

http://www.fwi.co.uk/articles/28/08/2013/140758/researchers-close-in-on-worm-resistance-in-sheep.htm

http://www.sciencedaily.com/releases/2013/08/130828103351.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily%2Fplants_animals+(ScienceDaily%3A+Plants+%26+Animals+News)

Image source: Wikipedia

Link:

http://www.nytimes.com/2013/11/21/science/two-surprises-in-dna-of-boy-found-buried-in-siberia.html?_r=0

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12736.html

“In humans and most vertebrates the duplication events were older so there are fewer duplicated genes still present. Most of the duplicated genes get lost or modified so much that they are no longer recognizable as duplicates over time. In the trout and salmon we can see an earlier stage in the process and many duplicated genes are still present,” said Dr Gary Thorgaard of Washington State University, a co-author of the paper published in the journal Nature Communications.

Source:

http://www.sci-news.com/genetics/science-genome-rainbow-trout-01877.html

Source:

http://news.ufl.edu/2014/04/28/real-time-genome-sequencing-at-sea/

miRNA Sequencing data analysis : http://tools.genxpro.net/omiras/

miRNApath( R based tool) :  http://www.bioconductor.org/packages/release/bioc/html/miRNApath.html

miRWalk DB : http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/

TargetScanHuman : http://www.targetscan.org/

RNAhybrid : http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/welcome.html

RNA22 predictor : http://cbcsrv.watson.ibm.com/rna22.html

miRNA predictor : http://www.microrna.org/microrna/home.do

Plant miRNA DB :http://bioinformatics.cau.edu.cn/PMRD/

miRBASE DB: http://www.mirbase.org/

Plant RNA predictor : http://plantgrn.noble.org/psRNATarget/

miRNA Interaction DB : http://starbase.sysu.edu.cn/

Sequencing based miRNA DB : http://mirgator.kobic.re.kr/

predicted A-to-I edited miRNA DB : http://microrna.osumc.edu/mireditar/

Animal, plant and virus miRNA DB : http://lemur.amu.edu.pl/share/php/mirnest/

Atlantic Salmon miRNAs DB : http://www.molgenv.com/ssa_mirnas_db_home.php

miRNA prediction on UTRs : http://genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html

Idea of analysing miRNA Sequencing data :

http://www.illumina.com/applications/epigenetics/small_rna_analysis.ilmn

More:

http://www.bioconductor.org/help/search/index.html?q=miRNA+target