2. In some perl script I found this
 . . . . . .
 . . . . . .
# It works for me, only God understood how it is working
while (/(<\/[^>]+>)|(<[^>]+>)|(<[^>]+>)$|([^><]+)/go) {
            $startGene=$1;
            $beginChromosome=$2;
    
. . . . . .
 .. . . . . .
}

3. One more interesting message in Perl found …. It will must tickle you bone :)
open(my $fh, "<", "gene.txt")    or kill " Me if you think this is a mistake :$!";

4. From the Perl

  while () {  # "The Mothership Connection is here!"
    print “$_\n”; # Printing the offspring :)

5. Perl message
if ($1) { print “Just found a the error in chromosome !!!, yahoo…”; else { “That is not error, but mutation you moron!”;

6. One genome database curator walk in wine bar asked the bartender:
CREATE TABLE gene IF NOT EXISTS SexOnTheBeach;

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.

Lab page: http://jarvislab.net/

• Whole-genome sequencing
• Whole-exome sequencing
• RNA-seq (speical mode available)
• ChIP-seq

Address of the bookmark: http://qualimap.bioinfo.cipf.es/

Applications are invited on prescribed format along with self attested copies of the certificates for Faculty Positions in the following areas:
Sciences – Systems Biology, Computer Aided Drug Designing, Statistics, Applied Mathematics, Applied Physics. BioMedical Engineering – BioMechanics, BioMedical Instrumentation.

Last Date : May 10, 2014

Details are available on our website : http://www.iiita.ac.in

http://www.iiita.ac.in/downloads/announcements/uploads/FACULTY_Advertisement_NO-FS-01_2014130.pdf

A swift, versatile phylogenomic and high-throughput sequencing simulator https://jackalope.lucasnell.com

Address of the bookmark: https://github.com/lucasnell/jackalope

Part I – Basic Find Commands for Finding Files with Names
1. Find Files Using Name in Current Directory

Find all the files whose name is gene.txt in a current working directory.

# find . -name gene.txt

./gene.txt

2. Find Files Under Home Directory

Find all the files under /home directory with name gene.txt.

# find /home -name gene.txt

/home/gene.txt

3. Find Files Using Name and Ignoring Case

Find all the files whose name is gene.txt and contains both capital and small letters in /home directory.

# find /home -iname gene.txt

./gene.txt
./Gene.txt

4. Find Directories Using Name

Find all directories whose name is Gene in / directory.

# find / -type d -name Gene

/Gene

5. Find fasta Files Using Name

Find all php files whose name is gene.fasta in a current working directory.

# find . -type f -name gene.fasta

./gene.fasta

6. Find all PHP Files in Directory

Find all fasta files in a directory.

# find . -type f -name "*.fasta"

./gene.fasta
./cancer.fasta
./allgene.fasta

Part II – Find Files Based on their Permissions
7. Find Files With 777 Permissions

Find all the files whose permissions are 777.

# find . -type f -perm 0777 -print

8. Find Files Without 777 Permissions

Find all the files without permission 777.

# find / -type f ! -perm 777

9. Find SGID Files with 644 Permissions

Find all the SGID bit files whose permissions set to 644.

# find / -perm 2644

10. Find Sticky Bit Files with 551 Permissions

Find all the Sticky Bit set files whose permission are 551.

# find / -perm 1551

11. Find SUID Files

Find all SUID set files.

# find / -perm /u=s

12. Find SGID Files

Find all SGID set files.

# find / -perm /g+s

13. Find Read Only Files

Find all Read Only files.

# find / -perm /u=r

14. Find Executable Files

Find all Executable files.

# find / -perm /a=x

15. Find Files with 777 Permissions and Chmod to 644

Find all 777 permission files and use chmod command to set permissions to 644.

# find / -type f -perm 0777 -print -exec chmod 644 {} \;

16. Find Directories with 777 Permissions and Chmod to 755

Find all 777 permission directories and use chmod command to set permissions to 755.

# find / -type d -perm 777 -print -exec chmod 755 {} \;

17. Find and remove single File

To find a single file called gene.txt and remove it.

# find . -type f -name "gene.txt" -exec rm -f {} \;

18. Find and remove Multiple File

To find and remove multiple files such as .fa or .gb, then use.

# find . -type f -name "*.fa" -exec rm -f {} \;

OR

# find . -type f -name "*.gb" -exec rm -f {} \;

19. Find all Empty Files

To file all empty files under certain path.

# find /tmp -type f -empty

20. Find all Empty Directories

To file all empty directories under certain path.

# find /tmp -type d -empty

21. File all Hidden Files

To find all hidden files, use below command.

# find /tmp -type f -name ".*"

Part III – Search Files Based On Owners and Groups
22. Find Single File Based on User

To find all or single file called gene.txt under / root directory of owner root.

# find / -user root -name gene.txt

23. Find all Files Based on User

To find all files that belongs to user Rahul under /home directory.

# find /home -user rahul

24. Find all Files Based on Group

To find all files that belongs to group Developer under /home directory.

# find /home -group developer

25. Find Particular Files of User

To find all .txt files of user Rahul under /home directory.

# find /home -user rahul -iname "*.txt"

Part IV – Find Files and Directories Based on Date and Time
26. Find Last 50 Days Modified Files

To find all the files which are modified 50 days back.

# find / -mtime 50

27. Find Last 50 Days Accessed Files

To find all the files which are accessed 50 days back.

# find / -atime 50

28. Find Last 50-100 Days Modified Files

To find all the files which are modified more than 50 days back and less than 100 days.

# find / -mtime +50 –mtime -100

29. Find Changed Files in Last 1 Hour

To find all the files which are changed in last 1 hour.

# find / -cmin -60

30. Find Modified Files in Last 1 Hour

To find all the files which are modified in last 1 hour.

# find / -mmin -60

31. Find Accessed Files in Last 1 Hour

To find all the files which are accessed in last 1 hour.

# find / -amin -60

Part V – Find Files and Directories Based on Size
32. Find 50MB Files

To find all 50MB files, use.

# find / -size 50M

33. Find Size between 50MB – 100MB

To find all the files which are greater than 50MB and less than 100MB.

# find / -size +50M -size -100M

34. Find and Delete 100MB Files

To find all 100MB files and delete them using one single command.

# find / -size +100M -exec rm -rf {} \;

35. Find Specific Files and Delete

Find all .gb files with more than 10MB and delete them using one single command.

# find / -type f -name *.gb -size +10M -exec rm {} \;

More at https://github.com/ekg/mutatrix

./mutatrix -S sample -P test/ -p 2 -n 10 reference.fasta

Address of the bookmark: https://github.com/ekg/mutatrix

Find more at http://www.imtech.res.in/raghava/

Orthology relations can be used to transfer annotations from one gene (or protein) to another. Hence, detecting orthology relations has become an important task in the post-genomic era. Various genomic events, such as duplication and horizontal gene transfer, can cause erroneous assignment of orthology relations. In closely-related species, gene neighborhood information can be used to resolve many ambiguities in orthology inference. Here we present OrthoGNC, a software for accurately predicting pairwise orthology relations based on gene neighborhood conservation. Analyses on simulated and real data reveal the high accuracy of OrthoGNC. In addition to orthology detection, OrthoGNC can be employed to investigate the conservation of genomic context among potential orthologs detected by other methods. OrthoGNC is freely available online at http://bs.ipm.ir/softwares/orthognc and http://tinyurl.com/orthoGNC.

http://www.comp.nus.edu.sg/~wongls/projects/orthoGNC/

Address of the bookmark: http://www.sciencedirect.com/science/article/pii/S1672022917301663