sed ':a;N;$!ba;s/\n//g' filename.txt > newfilename_oneline.txt

To get average for a column of numbers (here the second column $2):

awk '{ sum += $2; n++ } END { if (n > 0) print sum / n; }'

To get sequence length for all sequences in a fasta file:

awk '/^>/ {if (seqlen){print seqlen}; print ;seqlen=0;next; } { seqlen = seqlen +length($0)}END{print seqlen}' \
filename.fasta

To copy (move, rename, etc) files based on their list in a text file:

cat file_list.txt | while read line; do cp "$line" complete_dataset/"$line"; done

To split bam files into sets with mapped and unmapped reads:

samtools view -F4 sample.bam > sample.mapped.sam
samtools view -f4 sample.bam > sample.unmapped.sam

To gzip all your fastq files using gnu parallel and gzip:

parallel gzip ::: *.fastq

To gzip all your fastq files using pigz:

pigz *.fastq

To count all sequences in a fasta file:

grep "^>" yourfile.fasta -c

To count all sequences in all fasta files in your current directory:

for a in *.fasta; do ls $a; grep "^>" -c $a; done

To keep only one copy of duplicated lines:

awk '!seen[$0]++'

To sum assembly size from SPAdes contigs.fasta or scaffolds.fasta file:

grep "^>" scaffolds.fasta | cut -f 4 -d '_' | paste -sd+ | bc

To remove everything after the first space at each line, e.g. to to simplify fasta headers:

cut -d' ' -f1 < your_file

To count reads in a all .fastq.gz files in your current folder (fast, using gnu parallel):

parallel "echo {} && gunzip -c {} | wc -l | awk '{d=\$1; print d/4;}'" ::: *.gz

To count reads in a all .fastq.gz files in your current folder:

zcat *.gz | echo $((`wc -l`/4))

To count reads in a all .fastq files in your current folder:

cat *.fastq | echo $((`wc -l`/4))

To count base pairs in a all .fastq.gz files in your current folder:

zcat *.fastq.gz | paste - - - - | cut -f 2 | tr -d '\n' | wc -c 

To split multifasta file into many fasta files:

awk '/^>/ {OUT=substr($0,2) ".fa"}; {print >> OUT; close(OUT)}' Input_File

To convert Illumina FASTQ 1.3 to 1.8:

sed -e '4~4y/@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghi/!"#$%&'\''()*+,-.\/0123456789:;<=>?@ABCDEFGHIJ/' f.fastq

To convert FASTQ to FASTA:

sed -n '1~4s/^@/>/p;2~4p' 

To get fastq read length distribution:

cat reads.fastq | awk '{if(NR%4==2) print length($1)}' | sort | uniq -c

To deinterleave interleaved fastq file:

cat myf.fq | paste - - - - - - - - | tee >(cut -f 1-4 | tr "\t" "\n" > myfile_1.fq) | cut -f 5-8 | \
tr "\t" "\n" > myf2.fq 

To filter and sort contig identifiers from SPAdes assembly (e.g. here lenght >= 4000 + coverage >=100):

grep "^>" scaffolds.fasta | sed s"/_/ /"g | awk '{ if ($4 >= 4000 && $6 >= 100) print $0 }' | sort -k 4 -n | \
sed s"/ /_/"g

To append something to all headers of your fasta files:

sed 's/>.*/&YOURSTRING/' filename.fasta > new_filename.fasta

To replace/squeeze multiple adjacent spaces by only one space: 

tr -s " " < file

To filter fastq based on length (here larger than or equal to 21, but smaller than or equal to 25.

cat your.fastq | paste - - - - | awk 'length($2)  >= 21 && length($2) <= 25' | sed 's/\t/\n/g' > filtered.fastq

To print difference between the last and first row in 5th column:

awk '{if (!first){first=$5;}; last=$5;} END {print last-first}' myfile.txt

To sample only 200 first bases from all sequences in a multifasta file (e.g. from assembly scaffolds.fasta file here):

awk '/^>/{ seqlen=0; print; next; } seqlen < 200 { if (seqlen + length($0) > 200) $0 = substr($0, 1, 200-seqlen);\
 seqlen += length($0); print }' scaffolds.fasta > 200bp_scaffolds.fasta

 To pipe a compressed fasta file directly into makeblastdb.

gunzip -c fasta.gz | makeblastdb -in -

To remove sequences with duplicate fasta headers from a fasta file.

awk '/^>/{f=!d[$1];d[$1]=1}f' in.fasta > out.fasta

What is de novo genome assembly?
The method of taking a large number of short DNA sequences and placing them back together to create a reflection of the original chromosomes from which the DNA originated relates to genome assembly. No previous knowledge of the source DNA sequence length, structure or composition is inferred by De novo genome assemblies. The DNA of the target organism is split up into millions of tiny parts and read on a sequencing computer in a genome sequencing experiment. Depending on the sequencing system used, these "reads" range from 20 to 1000 nucleotide base pairs (bp) in length. Usually, length reads of 36 - 150 bp are produced for Illumina style short read sequencing. These reads can be either “single ended” as described above or “paired end.”

Why genome assembly?
In basic research into why and how they live, as well as in applied topics, identifying the DNA sequence of an organism is useful. Awareness of a DNA sequence may be useful in virtually any biological research because of the relevance of DNA to living things. For example, it may be used in medicine to classify, diagnose and eventually improve genetic disorder therapies. Similarly, pathogens study can lead to treatments for infectious diseases.

Raw NGS data
Reads can be saved as a Fasta file as text or in a FastQ file with their attributes. FastQ is the most common read file format since this is what the Illumina sequencing pipeline creates. This will henceforth be the subject of our conversation.

In a nutshell the protocol:
Get the sequence file(s) read from the sequencing machine (s).
Look at the readings - have an idea of what you have and what the standard is like.
If required, raw data cleanup/quality trimming.
Choose an adequate parameter set for assembly.
Assemble the data into scaffolds/contigs.
Examine the assembly performance and determine the efficiency of the assembly.

Read Quality Control:
Check the qualiy with fastQC.
Script
https://bioinformaticsonline.com/snippets/view/42540/install-fastqc-using-conda

Quality trimming/cleanup of read files.
This function trims adapters, barcodes and other contaminants from the reads.
Script
https://bioinformaticsonline.com/snippets/view/42542/trimmomatic-command

Genome Assembly:
The object of this portion of the protocol is to explain the method of assembling the reads trimmed by quality into draft contigs.

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o result_of_spades_assembly_all_illumina

A significant range of short-read assemblers are available. Everyone with strengths and disadvantages of their own.
Some of the assemblers available include:
Velvet
SOAP-denovo
MIRA
ALLPATHS

Next step is to assess the suitability and what to do with a draft package of contiguous details for the remainder of the study now. Few stuff you can note about the contigs you just created: They're the draft Contigs. Any mis-assemblies can occur.

Mis-assembly checking and assembly metric tools:
QUAST - Quality assessment tool for genome assembly http://bioinf.spbau.ru/quast
Mauve assembly metrics - http://code.google.com/p/ngopt/wiki/How_To_Score_Genome_Assemblies_with_Mauve
InGAP-SV - https://sites.google.com/site/nextgengenomics/ingap and http://ingap.sourceforge.net/
inGAP is also useful for finding structural variants between genomes from read mappings.

Genome finishing tools:
Semi-automated gap fillers:
Gap filler - http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/gapfiller/

IMAGE (V2) - http://sourceforge.net/apps/mediawiki/image2/index.php?title=Main_Page

Genome visualisers and editors:
Artemis - http://www.sanger.ac.uk/resources/software/artemis/
IGV - http://www.broadinstitute.org/igv/

Automated and semi automated annotation tools:
Prokka - https://github.com/tseemann/prokka
RAST - http://www.nmpdr.org/FIG/wiki/view.cgi/FIG/RapidAnnotationServer
JCVI Annotation Service - http://www.jcvi.org/cms/research/projects/annotation-service/

Frequent command use for the analysis are at:

https://bioinformaticsonline.com/blog/view/38765/list-of-tools-frequently-used-while-genome-assembly
https://bioinformaticsonline.com/pages/view/42275/frequent-parameters-for-bioinformatics-tools

Pockets Identification

CASTp

Pocket-Finder

PocketPicker

More at https://jobs.sanger.ac.uk/vacancy/senior-bioinformatician-assembly-moore-aquatic-symbiosis-project-tree-of-life-458923.html

Many times in bioinformatics we need to deal with huge datasets which  are more than 100GB size. The traditional way to analysis a file is using the while loop

while (FILE){

Do something;

}

This is very slow(since we are using only one processor) and if we have 500 million lines in the dataset it takes more than a day to iterate through the whole dataset. So how do we make best use of all our processors and get the work done quickly?

Here is a very simple and efficient technique with perl which i have been using. I am  more inclined towards using perl fork than perl threads.

One of the oldest way to fork is

my $fork = fork();
if($fork){   
push (@childs,$fork); 
}
elseif($fork==0){
your code here;
exit(0);
}
else{die “Couldnt fork : $!”;}

## wait for the child process to finish
foreach(@childs){
my $tmp=waitid($_,0);
}

what a fork does is it creates a child process and takes the variables and code with it to analyze it separately (detached from the parent process) and thus a separate process is created( which usually runs on a separate processor). Thats it!! One big disadvantage of forking is its very difficult to share variables among the different processes. I will show you how to do it easily but still it has its own drawbacks.

Okie, now if you really do not want to use fork in your code, that’s okie too..There are many useful modules which do it for you very efficiently. One really useful module is Parallel::ForkManager. You can use Parallel::ForkManager to manage the number of forks you want to generate (number of processors you want to use).

Simple usage:
use Parallel::ForkManager;
my $max_processors=8;
my $fork= new Parallel::ForkManager($max_processors);
foreach (@dna) {
$fork->start and next; # do the fork
you code here;
$fork->finish; # do the exit in the child process
}
$pm->wait_all_children;

so you will be generating 8 forks which do the same thing for your each element of array. when one child finishes, Parallel::ForkManager generates a new one and thus you will be using all your processors to analyze the data. Now, if you have generated 8 child processes and want to write the data to one file. You need to lock the file to do this, because you will have problems with the buffering. You can lock the file using flock command.

open (my $QUAL, “myfile.txt”);
flock $QUAL, LOCK_EX or die “cant lock file $!”;
print $QUAL “$output”;
flock $QUAL, LOCK_UN or die “$!”;
close $QUAL;

I would not suggest using flock when dealing with multiple processes because it will decrease the processing efficiency( each child process must wait for the lock to be released by the other child process). Instead, I would suggest each fork writing to a separate file and after the processing just concatenating them.

Putting it all together, If you have 100GB data you can do this

step 1 : split the dataset equally according to number of processors you have. this may take a few hours(about 2-3 hrs for 100GB file)
You can use unix “split” command for this
for example:
my $number_split=int($number_of_entries_in_your_dataset/$max_processors);
my $split_Files=`split -l $number_split “your_file.fasta” “file_name”`;

step2: open you directory comtaining you split files and start Parallel::ForkManager.
For example:
opendir(DIRECTORY, $split_files_directory) or die $!; ### open the directory
my $fork= new Parallel::ForkManager($max_processors);
while (my $file = readdir(DIRECTORY)) { ### read the directory
if($file=~/^\./){next;}
print $file,”\n”;
########## Start fork ##########
my $pid= $super_fork->start and next;
Whatever you want to do with the split file ;
analyze my piece of $file;
######### end fork ###############
$super_fork->finish;
}
$super_fork->wait_all_children;

So basically each processor will be active with its piece of data (split file) and thus you have created 8 processes at one time which run without interfering with the other process. I again will not suggest writing output from each child process to one file(for reasons above). Write output from each fork to a separate file and finally concatenate them. Thats it, you have just increased your program speed by 8 times!! Isnt it easy?

Note:
You may worry about concatenation of the output each child generates, since it does take some time(remember 100GB). I think now you can use a mysql database LOAD DATA LOCAL INFILE command to load all the files into a single table(Should take about 3hrs for 100Gb dataset) and then export the whole table into one file. This should be faster than just concatenating them using “cat” command.(correct me if I am wrong)

Or much simpler way is to use pipes

cat output_dir/* | my_pipe or my_pipe <(file1) final_file;

Thats it guys!! Enjoy programming and please do comment. I am not a computer scientist so forgive me for any mistakes and if any please report them. Thank you.

Effective July 10, 2023, NCBI’s Assembly and Genome record pages now redirect to new NCBI Datasets pages. As previously announced, these updates are part of our ongoing effort to modernize and improve your user experience. NCBI Datasets is a new resource that makes it easier to find and download genome data.   

The following pages have been updated:

• The NCBI Assembly record pages now redirect to the new NCBI Datasets Genome record pages that describe assembled genomes and provide links to related NCBI tools such as Genome Data Viewer and BLAST.  
• The NCBI Genome record pages now redirect to the NCBI Datasets Taxonomy record pages that provide a taxonomy-focused portal to genes, genomes, and additional NCBI resources.   

During this transition, you will have the option to return to the legacy Genome and Assembly record pages. We will remove the legacy pages in early 2024.  

Requirements
- 3-5 years of relevant bioinformatics experience such as public data mining,
processing, analysing and visualising omics data, etc.
- Ph.D., Masters or Bachelors in Bioinformatics, Biotechnology,
Computational Biology, or related field.
- Understanding of molecular biology and biochemistry.
- Comfort and experience with biological research and data.
- Proficient in a programming language used for bioinformatics such as R or
python.
- Excellent communication skills.
- Ability to summarise and simplify complex analyses for a non-technical
audience.
- Strong analytical skills, curiosity and a knack to solve difficult problems.
- Work well in multi-disciplinary teams with people of vastly different
backgrounds.
- Demonstrated success in collaboration and independent work.

More at https://angel.co/elucidata/jobs/460104-senior-bioinformatics-scientist

What is Genome Assembly?

Genome assembly involves piecing together short DNA sequence reads generated by sequencing platforms (e.g., Illumina, PacBio, Oxford Nanopore) into longer, contiguous sequences called contigs. This can be performed as:

• De Novo Assembly: Without a reference genome.
• Reference-Guided Assembly: Using a reference genome to guide the assembly process.

Step 1: Preparing Your Data

Before starting the assembly, ensure that your raw sequencing data is high quality.

1. Input Data

   • Short Reads: Illumina sequencing generates short, accurate reads ideal for scaffolding.
   • Long Reads: PacBio and Nanopore sequencing provide long reads for resolving repetitive regions.

2. Quality Control (QC)
   Use tools like FastQC or MultiQC to assess the quality of your reads:

   fastqc reads.fastq multiqc .

   Look for issues like low-quality bases, adapter contamination, or overrepresented sequences.

3. Read Trimming and Filtering
   Trim low-quality bases and adapters using Trimmomatic or Cutadapt:

   trimmomatic PE reads_R1.fastq reads_R2.fastq trimmed_R1.fastq trimmed_R2.fastq \ ILLUMINACLIP:adapters.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36

Step 2: Choosing an Assembly Strategy

Select an assembly strategy based on your data type:

• Short-Read Assemblers:

  • SPAdes: Popular for microbial genomes.
  • Velvet: Fast for smaller genomes.

• Long-Read Assemblers:

  • Canu: Ideal for long-read datasets.
  • Flye: Versatile for small and large genomes.

• Hybrid Assemblers:

  • MaSuRCA: Combines short and long reads.
  • Unicycler: Optimized for bacterial genomes.

Step 3: Running the Assembly

3.1. SPAdes (Short-Read Assembly)

SPAdes is an excellent choice for small genomes, such as bacteria.

The output includes assembled contigs (contigs.fasta) and scaffolds (scaffolds.fasta).

3.2. Canu (Long-Read Assembly)

Canu is designed for high-error long reads from PacBio or Nanopore.

The output will be in canu_output/genome.contigs.fasta.

3.3. Hybrid Assembly with Unicycler

Unicycler combines short and long reads for improved assemblies.

Step 4: Assessing Assembly Quality

After assembly, evaluate its quality using the following tools:

1. QUAST
   QUAST generates assembly statistics, such as N50, genome size, and GC content:

   quast contigs.fasta -o quast_output

2. BUSCO
   BUSCO checks genome completeness by identifying conserved genes:

   busco -i contigs.fasta -o busco_output -l fungi_odb10 -m genome

3. Assembly Graph Visualization
   Visualize assembly graphs with Bandage:

   Bandage load assembly_graph.gfa

Step 5: Post-Assembly Steps

1. Polishing
   Improve assembly accuracy using tools like Pilon (for short reads) or Racon (for long reads).

   racon long_reads.fasta mapped_reads.sam contigs.fasta > polished_contigs.fasta

2. Scaffolding
   Link contigs into scaffolds using tools like SSPACE or Opera-LG if required.

3. Annotation
   Annotate the assembled genome using Prokka for prokaryotes or Maker for eukaryotes.

   prokka --outdir annotation_output --prefix genome contigs.fasta

Step 6: Sharing and Archiving

1. Submit to Public Repositories
   Share your assembly in databases like NCBI GenBank, ENA, or DDBJ.

2. Metadata Preparation
   Include detailed metadata for your submission, such as organism name, sequencing platform, and coverage.

Best Practices

• Always perform quality checks at each stage to ensure data integrity.
• Use multiple tools to cross-validate results when working with complex genomes.
• Document parameters and software versions for reproducibility.

Conclusion

Genome assembly is a powerful process that transforms raw sequencing data into a coherent representation of an organism’s genome. By following this step-by-step guide, you can successfully assemble genomes and uncover valuable biological insights. Whether you’re assembling a microbial genome or tackling the complexities of a eukaryotic genome, these tools and strategies will set you on the path to success.

Also, Bioinformatics Industrial Training Program (BIITP) is merged with the HRD Biotechnology Industrial Training Programme (BITP).

While this comes as a surprise for a lot of participants. We believe this is a good attempt to unify and create a national benchmark for talent. And we appreciate this endeavor from Department of biotechnology.

However, such last-minute announcements can create confusion. Thus candidates are advised to go through the complete notification DBT-BET JRF 2019 via the link below.If you have any kind of doubts, you must contact DBT JRF or Biotecnika for any kind of help & assistance.

Attention:-Bioinformatics Programs (BINC and BIITP)

1. Bioinformatics National Certification (BINC) has been merged with DBT-Junior
Research Fellow (BET Exam)

2. Bioinformatics Industrial Training Program (BIITP) is merged with HRDBiotechnology Industrial Training Programme (BITP).

Students of Bioinformatics, who are interested to apply for Fellowship or Industrial
Training may keep track of the advertisement of DBT-JRF (BET Exam) and BITP
of DBT.

 More at http://www.bcil.nic.in/files/Attention_Bioinformatics_Programs_(BINC_and_BIITP).pdf