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

Young Professional II (Computer Science/Applications)
Master degree in Computer Science/Computer Applications/B.Tech (Computer Science) or equivalent.
Desirable: 1. Knowledge of Statistical and Computational Genomics/ Proteomics/ Bioinformatics/Data mining tools. 2. Experience in handling HPC, programming languages and database management packages.
A consolidated salary of Rs.25,000/- per month.
21 to 45 year

Young Professional II (Biotechnology/ Bioinformatics)
Master degree in Bioinformatics/ Biotechnology/ B. Tech(Biotech) or equivalent. Desirable: 1. Knowledge of Computational Genomics/Proteomics/Bioinformatics. 2. Expertise in NGS data analysis and knowledge of allied software and tools.
A consolidated salary of Rs.25,000/- per month.

Senior Research Fellow
1. Bachelors degree with Zoology, Fisheries and 2. Master's degree in Fishery science/ Zoology with Fisheries/ Biotechnology/ Life Sciences with specialization in Fisheries/ Molecular Biology. 3. 1 st Division or 60% marks or equivalent overall grade point average.
Desirable: Work experience in Fisheries, molecular research techniques, bioinformatics and Computer skills. NET qualified
Note: The project involves extensive exploration tours and sampling from water bodies all over India
Rs.16,000/- p.m. for 1st & 2nd year and `18,000/- p.m. for 3rd and subsequent years +HRA (as per rules) 35 years for male and 40 years for female candidate

How to apply

A walk-in-interview will be held on 04th March, 2015 at 10:00 hrs at National Bureau of Fish Genetic Resources, Lucknow. Eligible and desirous candidates fulfilling all the requirements may appear for the interview with duly filled in application giving full details of academic records and experience(s) along with attested photocopy as well as original copy of the relevant documents and a passport size photograph on the attached proforma.

http://www.nbfgr.res.in/

More at https://ucdavis-bioinformatics-training.github.io/

Address of the bookmark: https://ucdavis-bioinformatics-training.github.io/2020-Genome_Assembly_Workshop/snakemake/snakemake_intro

It is possible to create a vector by typing data directly into R using the combine function ‘c’

x

same as

x

creates the vector x with the numbers between 1 and 5.

You can see what is in an object at any time by typing its name;

x

will produce the output ‘[1] 1 2 3 4 5′

Note that names need to be quoted

daysofweek ← c(‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’);

Usually however you want to input from a file. We have touched on the ‘read.table’ function already.

mydata

Now mydata is a data frame with multiple vectors

each vector can be identified by the default syntax

#if any of these are typed it will print to screen

mydata$V1 mydata$V2 mydata$V3

By default the function assumes certain things from the file

• The file is a plain text file (there are function to read excel files: not covered here)
• columns are separated by any number of tabs or spaces
• there is the same number of data points in each column
• there is no header row (labels for the columns)
• there is no column with names for the rows** [I’ll explain].

If any of these are false, we need to tell that to the function

If it has a header column

mydata header=T also works

Note that there is a comma between different parts of the functions arguments

If there is one less column in the header row, then R assumes that the 1^st column of data after the header are the row names

Now the vectors (columns) are identified by their name

#if any of these are typed it will print to screen

mydata$A mydata$B mydata$C

# Summary about the whole data frame

summary(mydata)

# Summary information of column A

summary(mydata$A)

We can shortcut having to type the data frame each time by attaching it

attach(mydata)

# summary of column B as ‘mydata’ is attached

summary(B)

Two other important options for read.table

If is is separated only by tabs and has a header

mydata

Really useful if you have spaces in the contents of some columns, so R does not mess up reading the columns . However if the columns or of an uneven length it will tell you.

If you know that the file has uneven columns

mydata

This causes R to fill empty spaces in a columns with ‘NA’ .

The last two examples will still work with our file and give the same result as with only headers=T

Graphs

to get an idea of what R is capable of type

demo(graphics)

steps through the examples, and the code is printed to the screen

We will work with simpler examples that have immediate use to biologists.

Remember to get more information about the options to a function type ‘?function’

Histogram of A

hist(mydata$A)

If there was more data we could increase the number of vertical columns with the option, breaks=50 (or another relevant number).

boxplot(mydata)

We can get rid of the need to type the data frame each time by using the attach function

# if not already done so

attach(mydata)

boxplot(mydata$A, mydata$B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

same as

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Scatter plot

# if not already done so

attach(mydata)

plot(A,B) # or plot(mydata$A, mydata$B)

SAVING an image

Windows users (Rgui) RIGHT click on image and select which you want.

These instructions work for everyone.

You need to create a new device of the type of file you need, then send the data to that device

to save as a png file (easy to load into the likes of powerpoint, also great for web applications.

png(‘filename’)

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

or to save as a pdf

pdf(‘filename’)

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Note

• Nothing will appear on screen, the output is going to the file
• Also it may not be saved immediately but will once the device (or R) is turned quit.

To quit R type

q() # If you save your session, next time you start R, you will have your data preloaded.

Or if you want to remain in R

dev.off() #turns of the png (or pdf etc) device, thus forces the data to save

Address of the bookmark: https://github.com/cschin/Peregrine

No of post : 01
Essential qualifications: i) Ph. D degree in Bioinformatics/computers/Bio-technology. OR ii) Master’s Degree in Bioinformatics/computers/Bio-technology with 1st division or 60% marks or equivalent overall grade point average with at least two years of research experience as evidenced from fellowship/Associateship/training/other engagements.
Desirable qualifications: i) Working Knowledge and Published Research papers in Bio-informatics.
Monthly emoluments : Rs. 23,000/- + HRA . for M.Sc degree holder Rs. 24,000/- + HRA for Ph.D degree holder
Maximum Age limit : Research Associate – Males- 40 years & Women 45 years.
SELECTION PROCEDURE FOR CENTRAL POTATO RESEARCH INSTITUTE (CPRI) – RESEARCH ASSOCIATE POST:

Written Test on 20/03/2015.
Shortlisted candidates will undertake face to face interview.
Dates are yet to be announced for the final selection
WALK-IN PROCEDURE FOR RESEARCH ASSOCIATE VACANCY IN CENTRAL POTATO RESEARCH INSTITUTE (CPRI):

Interested/eligible candidates should submit their application along with the attested copies of educational qualification (provisional degree of Masters and Ph.D is mandatory )/experience certificates and one passport size photograph to the Asstt. Admn. Officer(E-I), CPRI, Shimla-171001 at 9.30 AM on the date of interview. The candidates appearing for interview must bring original certificate with them and only those candidates possessing essential qualification as per advertisement will be interviewed. The Director, CPRI, Shimla reserves the right either to fill up the post or cancel the interview without assigning any reasons thereof. Application form is available in the website ( website: http//cpri.ernet.in). No TA/DA will be given by the Institute to the candidates. The Institute is located at Bemloe which is about 2 Kms from Main Bus Stand(Old)/3 Kms. from the Railway Station and about 5 Kms. from ISBT (Tutikandi).

https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

Address of the bookmark: https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

PRINCIPAL SCIENTIST Pay Band: Minimum pay of `43,000 in the PB-4 of `37400-67000/- + RGP of `10,000/-.

Age: The candidates must not have attained the age of 52 years as on 24.03.2015. There shall be no age limit for the Council’s employees.

ICAR-NATIONAL INSTITUTE OF BIOTIC STRESS MANAGEMENT, RAIPUR (CHHATTISGARH)

57. Principal Scientist (Agricultural Entomology) (Two post)

Qualifications Essential:

(i) Doctoral degree in Agricultural Entomology including relevant basic sciences.

(ii) 10 years experience in the relevant subject out of which at least 8 years should be as Scientist/ Lecturer/Extension Specialist or in an equivalent position in the Pay Band- 3 of `15600-39100 with Grade Pay of `5400/`6000/`7000/`8000 and 2 years as a Senior Scientist or in an equivalent position in the Pay Band- 4 of ` 37400-67000 with Grade Pay of ` 8700/ ` 9000.

(iii) The candidate should have made contribution to research/teaching/extension education as evidenced by published work/innovations and impact.

Desirable:

(i) Experience of using frontiers research tools in management of insect pests of crop plants.

(ii) Evidence of contributions to relevant field through publications/ patents/citation index to suggest a vision/perspective in biotic stress research.

61. Principal Scientist (Bioinformatics) (One post)

Qualifications Essential:

(i) Doctoral degree in Bioinformatics including relevant basic sciences. (ii) & (iii) As in item no. 57 above.

Desirable:

(i) Experience of using bioinformatics for advancement of knowledge and for research on biotic stress management.

(ii) Evidence of contributions to relevant field through publications/patents/citation index to suggest a vision/perspective in biotic stress research.

http://asrb.org.in/administrator/uploads_dir/1424859407english.pdf

Address of the bookmark: https://genomes.atcc.org/

ICAR-NPTC: Fibre development in flax/linseed.

(Job # 1) Research Associate (one) (Bioinformatics)

Rs.24000+ 30% HRA) for Ph.D. and for M. Sc Rs.23000/‐ (+ 30% HRA)

Ph.D. Degree in Bioinformatics/Molecular Biology/Biotechnology/ Genetics/allied sciences; or M. Sc in Bioinformatics/ Biotechnology/Life Sciences/ allied sciences with 1st division or 60% marks or equivalent overall grade point average with at least two years of research experience as evidenced from Fellowship/ Associate ship. 2 years research experience in bioinformatic data analysis/molecular biology techniques, and high throughput DNA/RNA sequencing, and transcriptome data analysis. Research paper with IF>1 will be desirable

ICAR-NPTC: Shade avoidance/low-light tolerance in rice.

General Terms & Conditions applicable to all the positions:
Age Limit: 35 years max. (5 years relaxation for SC/ST/OBC and woman candidates as per ICAR rules).
The positions are purely temporary, on a contractual basis and are initially offered for one year.
Originals must be shown for verification. 7. Research experience (Experience certificate from previous employer to be attached): I hereby declare that the information provided above is true to the best of my knowledge. Date: Signature

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