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

Before you go down that path, consider this critical biological question:
Are you sequencing human cells—or bacterial contamination?

The Silent Saboteur: Mycoplasma in Cell Cultures

Mycoplasma contamination remains one of the most widespread and underdiagnosed issues in tissue culture work. Studies suggest that 15–35% of cell lines in use may be contaminated, often without visible signs. Unlike other microbial infections, Mycoplasma does not produce cloudiness, odor, or a change in pH. Many researchers won’t detect it unless they specifically test for it.

The consequences, however, are profound. Mycoplasma can significantly alter:

• Host gene expression patterns

• Cell proliferation rates

• Epigenetic profiles and chromatin accessibility

• Cytokine signaling and immune responses

In short, it can skew your results, compromise your biological conclusions, and invalidate weeks or months of research.

A Simple Diagnostic Step: Map Against Mycoplasma Genomes

If you encounter poor alignment rates to the human genome, consider mapping your reads to a Mycoplasma reference genome—or better yet, use a combined human + Mycoplasma reference. There have been cases where over half of all reads, initially assumed to be from human cells, were in fact bacterial in origin. This check is fast, easy, and could save your project.

How Contamination Happens—and Persists

Mycoplasma is small (0.1–0.3 μm), lacks a cell wall, and can pass through standard filters undetected. Common sources include:

• Contaminated reagents (e.g., FBS)

• Infected cell lines obtained from other labs

• Poor aseptic technique or shared equipment

Once present, it spreads quickly between cultures and can persist for months, silently affecting results.

Why Treatment Is Difficult

While antibiotics such as Plasmocin or BM-Cyclin are sometimes used, they often offer only partial resolution and may themselves alter cell behavior. In many cases, the best course of action is to discard the contaminated culture and start with a fresh, verified stock.

Practical Recommendations for Researchers

• Routinely test for Mycoplasma using PCR, qPCR, or fluorescence-based assays

• Incorporate contamination screens into your sequencing QC pipeline

• Use combined reference genomes when mapping ambiguous reads

• Practice strict aseptic technique and monitor all incoming cell lines

• Don’t ignore unexplained data anomalies—they might point to contamination

Closing Thought: Contamination Is a Biological Variable

It’s easy to view poor mapping as a technical issue, but sometimes the problem lies deeper—in the biology itself. Mycoplasma contamination doesn’t just interfere with sequencing; it interferes with science. As a research community, we must treat contamination not as an afterthought, but as a key variable to control.

So next time your reads won’t align, don’t just tune the aligner. Ask if your cells are telling the truth—or if they're hiding something.

In the mid 1960's scientists discovered that many genomes contain stretches of highly repetitive DNA sequences ( see Reassociation Kinetics Experiments, and C-Value Paradox ). These sequences were later characterized and placed into five categories:

Simple Repeats - Duplications of simple sets of DNA bases (typically 1-5bp) such as A, CA, CGG etc.
Tandem Repeats - Typically found at the centromeres and telomeres of chromosomes these are duplications of more complex 100-200 base sequences.
Segmental Duplications - Large blocks of 10-300 kilobases which are that have been copied to another region of the genome.
Interspersed Repeats
Processed Pseudogenes, Retrotranscripts, SINES - Non-functional copies of RNA genes which have been reintegrated into the genome with the assitance of a reverse transcriptase.
DNA Transposons
Retrovirus Retrotransposons
Non-Retrovirus Retrotransposons ( LINES )

Currently up to 50% of the human genome is repetitive in nature and as improvements are made in detection methods this number is expected to increase.

On the other hand; In genetics, the term palindrome refers to a sequence of nucleotides along a DNA (deoxyribonucleic acid) or RNA (ribonucleic acid) strand that contains the same series of nitrogenous bases regardless from which direction the strand is analyzed. Akin to a language palindrome—wherein a word or phrase is spelled the same left-to-right as right-to-left (e.g., the word RADAR or the phrase "able was I ere I saw elba")—with genetic palindromes it does not matter whether the nucleic acid strand is read starting from the 3' (three prime) end or the 5' (five prime) end of the strand.

Recent research on palindromes centers on understanding palindrome formation during gene amplification. Other studies have attempted to relate palindrome formation to molecular mechanisms involved in double stranded breaks and in the formation of inverted repeats. Assisted by high speed computers, other groups of scientists link palindrome formation to the conservation of genetic information.

Related to the direction of transcription by RNA polymerase, DNA strands have upstream and downstream terminus defined by differing chemical groups at each end. The ends of each strand of DNA or RNA are termed the 5' (phosphate bound to the 5' position carbon) and 3' (phosphate bound to the 3' carbon) ends to indicate a polarity within the molecule. Using the letters A, T, C, G, to represent the nitrogenous bases adenine, thymine, cytosine, and guanine found in DNA, and the letters A, U, C, G to represent the nitrogenous bases adenine, uracil, cytosine, guanine found in RNA (Note that uracil in RNA replaces the thymine found in DNA), geneticists usually represent DNA by a series of base codes (e.g., 5' AATCGGATTGCA 3'). The base codes are usually arranged from the 5' end to the 3' end.

Because of specific base pairing in DNA (i.e., adenine (A) always bonds with (thymine (T) and cytosine (C) always bonds with guanine (G)) the complimentary stand to the sequence 5' AATCGGATTGCA 3' would be 3' TTAGCCTAACGT 5'.

With palindromes the sequences on the complimentary strands read the same in either direction. For example, a sequence of 5' GAATTC3' on one strand would be complimented by a 3' CTTAAG 5' strand. In either case, when either strand is read from the 5' prime end the sequence is GAATTC. Another example of a palindrome would be the sequence 5' CGAAGC 3' that, when reversed, still reads CGAAGC.

Palindromes are important sequences within nucleic acids. Often they are the site of binding for specific enzymes (e.g., restriction endobucleases) designed to cut the DNA strands at specific locations (i.e., at palindromes).

Palindromes may arise from brakeage and chromosomal inversions that form inverted repeats that compliment each other. When a palindrome results from an inversion, it is often referred to as an inverted repeat. For example, the sequence 5' CGAAGC 3', if inverted (reversed 180°), still reads CGAAGC.

The European Molecular Biology Open Software Suite (EMBOSS) includes some basic tools for finding tandem repeats and inverted repeats (see B.6.22. Applications in group Nucleic:repeats). There are many on-line services providing the EMBOSS tools, for example:

• Wageningen Bioinformatics Webportal EMBOSS explorer
• Mobyle@Pasteur
• Soaplab2 Web Services at Vital-IT

For more sophisticated repeat finding you will want to look at tools using Repbase for example:

• CENSOR
  • CENSOR@GIRI
  • CENSOR@EMBL-EBI
• RepeatMasker
• MUMmer (scan_for_match)
• Emboss Palindrome

Other nucleotide repeat finding methods found by a couple of web searches:

• Tandem Repeats Finder
• RECON
• RepeatRunner
• REPuter
• Imperfect Microsatellite Extractor (IMEx)
• Spectral Repeat Finder (SRF)
• REPFIND
• CRISPRfinder
• GrailEXP
• CONREPP
• find_palindromes
• Palindrome
• EMBOSS Palindrome
• Palindrome Search

Address of the bookmark: https://gvolante.riken.jp/analysis.html

GenBank release 257.0 (8/15/2023) is now available on the NCBI FTP site. This release has 25.10 trillion bases and 3.69 billion records.

The current release has:

• 246,119,175 traditional records containing 2,112,058,517,945 base pairs of sequence data
• 2,631,493,489 WGS records containing 22,294,446,104,543 base pairs of sequence data
• 686,271,945 bulk-oriented TSA records containing 646,176,166,908 base pairs of sequence data
• 124,421,006 bulk-oriented TLS records containing 48,289,699,026 base pairs of sequence data

Address of the bookmark: https://github.com/lh3/ropebwt2

Address of the bookmark: http://www.softberry.com/berry.phtml?topic=fgenesh&group=help&subgroup=gfind

http://cube.univie.ac.at/gepard

Address of the bookmark: https://github.com/univieCUBE/gepard