HECIL’s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.

Address of the bookmark: https://github.com/NDBL/HECIL

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

This blog provides a comprehensive step-by-step guide to detect piRNAs using bioinformatics tools and workflows.

Step 1: Prepare Your Data

1. Obtain RNA Sequencing Data
   Acquire raw small RNA-seq data in FASTQ format. Datasets can be sourced from repositories like NCBI SRA, EMBL-EBI, or specific small RNA sequencing projects.

2. Quality Control (QC)
   Use FastQC to assess the quality of raw reads:

   fastqc reads.fastq

   Evaluate the per-base quality, adapter content, and overrepresented sequences.

3. Trimming and Adapter Removal
   Use tools like Cutadapt or Trim Galore! to remove adapters and low-quality bases:

   cutadapt -a TGGAATTCTCGGGTGCCAAGG -o trimmed_reads.fastq reads.fastq

   Ensure the remaining reads are of high quality for downstream analysis.

Step 2: Map Reads to the Genome

Mapping reads to the reference genome is crucial for identifying piRNA loci.

1. Reference Genome Preparation
   Download the genome assembly of your organism from databases like Ensembl, UCSC Genome Browser, or NCBI.

2. Align Reads
   Use Bowtie or STAR for small RNA alignment:

   bowtie -v 1 -k 1 --best genome_index trimmed_reads.fastq -S aligned_reads.sam
   • -v 1: Allows one mismatch.
   • -k 1: Reports the best alignment.

3. Convert SAM to BAM
   Convert and sort alignments using SAMtools:

   samtools view -Sb aligned_reads.sam | samtools sort -o sorted_reads.bam

Step 3: Identify Small RNAs

piRNAs are characterized by their size (24–32 nt) and strand bias.

1. Extract Reads by Size
   Use tools like BEDtools or custom scripts to filter reads between 24 and 32 nt:

   bedtools bamtofastq -i sorted_reads.bam -fq all_reads.fastq seqkit seq -m 24 -M 32 all_reads.fastq > piRNA_size_reads.fastq

2. Check for Sequence Bias
   piRNAs often have a strong bias for a uridine at the 5’ end (1U bias). Use tools like WebLogo to visualize sequence motifs.

Step 4: Detect Ping-Pong Signature

The ping-pong amplification loop is a hallmark of piRNA biogenesis, characterized by a 10 nt overlap between piRNAs on opposite strands.

1. Generate Overlap Statistics
   Use the piPipes tool or custom scripts to calculate overlap:

   python ping_pong_overlap.py sorted_reads.bam

2. Visualize Overlap Distribution
   Plot the distribution of overlaps to confirm the presence of the 10 nt ping-pong signature.

Step 5: Annotate piRNA Clusters

piRNAs are often generated from genomic clusters.

1. Cluster Identification
   Use tools like proTRAC or PIRANHA to identify piRNA-producing clusters:

   proTRAC.pl -s sorted_reads.bam -g genome.fa -o clusters

2. Annotate Genomic Regions
   Annotate the identified clusters using gene annotation files (GTF/GFF). Tools like BEDtools intersect can help associate piRNA clusters with genes or transposable elements:

   bedtools intersect -a clusters.bed -b genome_annotation.gtf > annotated_clusters.bed

Step 6: Functional Analysis

Functional analysis of piRNAs can uncover their targets and regulatory roles.

1. Predict piRNA Targets
   Use tools like IntaRNA or RNAhybrid to predict interactions between piRNAs and potential target mRNAs:

   RNAhybrid -t target_transcripts.fa -q piRNAs.fa > piRNA_targets.txt

2. Enrichment Analysis
   Perform GO or KEGG enrichment analysis of target genes using tools like g:Profiler or DAVID.

Step 7: Validation and Visualization

1. Validate piRNA Candidates
   Cross-check the identified piRNAs against known piRNA databases, such as piRBase or piRNAdb.

2. Visualize Results

   • Use IGV (Integrative Genomics Viewer) to visualize piRNA alignment and clusters on the genome.
   • Generate heatmaps or circos plots to present piRNA distributions.

Step 8: Share and Publish Findings

1. Archive Data
   Submit sequencing data to public repositories like SRA or GEO with metadata specifying piRNA-related experiments.

2. Publish Results
   Share findings in journals or conferences, emphasizing novel piRNA candidates, target genes, or regulatory mechanisms.

Conclusion

Detecting piRNAs involves a combination of computational and analytical methods to identify these unique small RNAs and their roles in gene regulation and transposable element suppression. By following this step-by-step guide, you can confidently navigate the complexities of piRNA detection and contribute to the growing understanding of their biological significance.

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/

https://github.com/TF-Chan-Lab/OMTools

Address of the bookmark: https://github.com/TF-Chan-Lab/OMTools

LAST can:

• Handle big sequence data, e.g:
  • Compare two vertebrate genomes
  • Align billions of DNA reads to a genome
• Indicate the reliability of each aligned column.
• Use sequence quality data properly.
• Compare DNA to proteins, with frameshifts.
• Compare PSSMs to sequences
• Calculate the likelihood of chance similarities between random sequences.
• Do split and spliced alignment.
• Train alignment parameters for unusual kinds of sequence (e.g. nanopore).

Address of the bookmark: http://last.cbrc.jp/

Address of the bookmark: http://www.zhanyuwang.xin/wordpress/index.php/2017/07/21/baum-improving-genome-assembly-by-adaptive-unique-mapping-and-local-overlap-layout-consensus/

The successful candidate will be in charge of the detection of polymorphisms including structural variants, of the comparison of multiple and diverse genomes of a same species and of the construction of pan- and core-genomes. These challenging tasks will require bioinformatics developments and implementation of methods for accommodating the high level of repetitiveness of complex genomes. The tools will be integrated into pipelines and made available to end-users through the Galaxy platform. The bioinformatician will therefore also have to provide researchers with advices on their experimental designs in order to ensure compliance of produced datasets with pipelines requirements. He/she will be hosted by a bioinformatics/informatics team (7 people) (http://moulon.inra.fr/index.php/fr/equipestransversales/atelier-de-bioinformatique) which has computational facilities and expertise in NGS data analysis, and will benefit as well from national and international collaborative networks (Aplibio http://www.renabi.fr/platforms/aplibio/, Transplant http://transplantdb.eu, AMAIZING http://www.amaizing.fr/).

The position requires a doctoral degree (PhD) in bioinformatics with strong expertise in script writing (Python/Perl) and pipeline development.

Applicants should send a CV and the names of 2 referees willing to provide a letter of recommendation to joets@moulon.inra.fr.

Center for Integrative Genomics, University of Lausanne, Switzerland

Two postdoctoral positions (2 years with possible extensions up to 5 years) are available immediately in the evolutionary genomics group of Henrik Kaessmann.

We are seeking highly qualified and enthusiastic applicants with strong skills in computational biology/bioinformatics, preferably also with experience in data mining and comparative or evolutionary genome analysis.

We have been interested in a range of topics related to the functional evolution of genomes from primates (e.g., the emergence of new genes and their functions) and other mammals (e.g., the origin and evolution of mammalian sex chromosomes). In the framework of a recently launched series of projects, a large amount of transcriptome and genome (e.g., epigenome) data are being produced by the wet lab unit of the group using next generation sequencing technologies for a unique collection of tissues from representative mammals and outgroup species (e.g., birds). Topics of current projects based on these data include the origins and/or evolution of protein-coding genes, alternative splicing, microRNAs, long noncoding RNAs, and dosage compensation.

The postdoctoral fellow will perform integrated evolutionary/bioinformatics analyses based on data produced in the lab and available genomic data. The specific project will be developed together with the candidate.

The language of the institute is English, and its members form an international group that is rapidly expanding. The institute is located in Lausanne, a beautiful city at Lake Geneva.

For more information on the group and our institute more generally, please refer to our website: http://www.unil.ch/cig/page7858_en.html

Please submit a CV, statement of research interest, and names of three references to: Henrik Kaessmann (Henrik.Kaessmann@unil.ch).

Webpage : http://www.unil.ch/cig/page7858.html