This is the simple way to use the module

Example1:

use Benchmark;

$first_time = Benchmark->new;

our code……

$second_time = Benchmark->new;

$final_difference = timediff($first_time,$second_time);

print “the code took, timestr($final_difference),”\n”;

that was a very simple way to know the time diff , we can use it to know the time taken by some part of the code in the program.

More sophisticated way:

use Benchmark;
sub first {

my(arguments) = @_;

}

timethese(100, { first => ‘first_sub(arguments)’});

The first argument to timethese is 100 (evaluate 100 times).

Hope this very small tutorial with Benchmark will help people get started.

If you're interested in refreshing your memory or just new to the series, please check previous entries over here: 1 2 3

https://metacpan.org/pod/AI::MXNet

Address of the bookmark: http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html

Alternatively, use one liner perl script:

perl -ne 'if(/^>(\S+)/){$c=$i{$1}}$c?print:chomp;$i{$_}=1 if @ARGV' GIs.txt >sequence.fasta

where GIs.txt contains a list of GIs or accession IDs.

(from :http://edwards.sdsu.edu/labsite/index.php/robert?start=5)

Address of the bookmark: http://edwards.sdsu.edu/labsite/index.php/robert/380-ncbi-sequence-or-fasta-batch-download-using-entrez

To resolve this, download and have in SSPACED-Longreads folder.

Cheers :)

“The useful features of NgAgo for genome editing include the following.First, it has a low tolerance to guide–target mismatch. A single nucleotide mismatch at each position of the gDNA impaired the cleavage efficiency of NgAgo, and mismatches at three positions completely blocked cleavage in our experiments. Second, 5′ phosphorylated short ssDNAs are rare in mammalian cells, which minimizes the possibility of cellular oligonucleotides misguiding NgAgo.Third, NgAgo follows a ‘one-guide-faithful’ rule, that is, a guide can only be loaded when NgAgo protein is in the process of expression, and, once loaded, NgAgo cannot swap its gDNA with other free ssDNA at 37 °C. All of these features could minimize off-target effects. Finally, it is easy to design and synthesize ssDNAs and to adjust their concentration, which is difficult with the Cas9-sgRNA system, if the sgRNA is expressed from a plasmid and the normal dosage of an ssDNA guide is only ~1/10 of that of a sgRNA expression plasmid.

NgAgo might be a more orderly way and perhaps even simpler way to go about genome editing than CRISPR, but the jury is still out on that until there are more papers and data. The NgAgo edit efficiency at this preliminary stage of technology development seems very strong. See the pics below

Reference: http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3547.html

Address of the bookmark: https://bioinformaticsworkbook.org/dataAnalysis/GenomeAssembly/GeneticMaps/creating-genetic-maps.html

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

Address of the bookmark: https://portal.nersc.gov/genomad

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581