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If you weren't at AGBT, then you didn't get a change to see this poster. 

"Accurate detection of de novo mutations in rare and common neurodevelopmental disorders" was presented by collaborators from Radboud University Nijmegen Medical Centre.  The exomes were sequenced on the 5500xl W.

The poster is attached below.

Life Technologies is committed to supporting your research and continuing to simplify workflows by expanding the menu for the AB Library Builder™ System.  Today we are introducing the Whole Transcriptome Core Kit for 5500 Genetic Analysis Systems (PN 4472690) that with the AB Library Builder™ System can generate 13 whole transcriptome libraries, compatible with the SOLiD® 4 System and 5500 Series Genetic Analysis Systems in a single run with reduced hands-on time.

Don’t forget you can also prep DNA fragment libraries on the AB Library Builder™ System with kits for SOLiD® 4 System and 5500 Series Genetic Analysis Systems or using the protocol on the Ion Community for Ion Personal Genome Machine™ sequencer.

Ask your sales representative for a quote for the AB Library Builder™ System at the new reduced price.

A Cornell University food scientist has co-authored a paper titled, "Genome sequencing reveals diversification of virulence factor content and possible host adaptation in distinct subpopulations of Salmonella" which focuses on research that will help accelerate the identification of Salmonella outbreak strains.  As this research is well-timed given recent stories in the news, we talked to Henk den Bakker, the publication's lead author from Martin Wiedmann's lab, to get more information about the study.

Read the full publication

Dr. Hank den Bakker:

The paper describes how you used various methods to describe differences between S. enterica species and subclades.  Why is an understanding of the population structure important for human health?

In the case of S. enterica there seems to be a strong correlation between population structure, that is groups of closely related strains, and the distribution of virulence factors such as gene clusters involved in attachment to host tissues, gene clusters involved in the metabolism of host specific carbon sources and cytotoxins.  Now that we know that these subgroups differ in virulence factor content we can start to look for phenotypes of these two subgroups, first with in vitro studies in tissue culture, followed by studies in animal models, and subsequently look for specific clinical presentations that are potentially associated with infections of Salmonella of these different subclades. A better understanding of the relation between Salmonella subgroups and the types of disease they cause will provide a better understanding of how these subgroups impact human health and the food industry.

Could you describe why whole genome sequencing was needed to reach your conclusions?

By choosing whole genome sequencing combined with de novo assembly of the genomes we were able to get unique information that allowed us to get the ‘whole picture’.  We used the sequence information of a large number of genes of these de novo assemblies to infer the population structure and at the same compare these genomes for the presence/absence of all the genes in the complete genome, which makes it possible to see which genes are present or absent in certain subpopulations.  This is in contrast to reference based assemblies, which can help to get a detailed picture of the population structure based on SNP data, but lacks the ability to discover novel genes that are not in the reference genome. With other methods (e.g., serotyping, PFGE, MLST, MLVA, microarray) we never could have obtained this amount of information.

What evidence did you see that some S. enterica serovars are adapted to human hosts?

We currently have no evidence that certain serovars (except for Typhi and Paratyphi A) are more adapted to the human host, however we now know that certain virulence factors that are found in known ‘human restricted’ serovars such as Typhi and Paratyphi A are found in a much wider variety of serovars. Future research is needed to determine if and how these virulence factors may affect the ability of these serovars to interact  with human hosts.

What did you learn about virulence factors and are there any practical implications from it?

One of the things we learned about the evolution of virulence factors in this study was that certain virulence factors, that were assumed to be specific for S. Typhi, were possibly already present in the most recent common ancestor of S. enterica and were very unlikely to be acquired by recent horizontal gene transfer. We also learned that the two clades of Salmonella differ in some virulence factors, and this may have implications for clinical symptoms associated with disease caused by Salmonella enterica.

People will be want to learn about S. Typhi and its relationships to other S. enterica.  Does that explain any of its history and how it originated.

One of the things we established in this study was a good list of candidates of the serovars that are most closely related to S. Typhi.  Research on pathogenicity and the evolutionary origin of S. Typhi has mainly focused on comparisons of various strains representing S. Typhi and a distantly related serovar, S. Typhimurium.  Comparative genomic research of S. Typhi with a wider range of Salmonella serovars, allowed us to identify its nearest relatives. Further comparative analysis of S. Typhi with its closely related serovars will give us a more precise picture of how S. Typhi evolved into the severe human pathogen it is now.

LifeScope(TM) software 2.5 is now available. Here are two main features of the software update.

Low frequency variant detection workflow

The low frequency variant detection workflow uses the diBayes algorithm (with highly optimized parameters) to reveal SNPs present in a targeted resequencing experiment. The detection range can range from 1%-5% based on experimental conditions, acceptable sensitivity and specificity, and sequencing coverage. This workflow is ideal for detecting low frequency variants in cancer-related research projects.

(check out this video to hear more: http://solid.community.appliedbiosystems.com/videos/1160)

Processing time improvements

Release 2.5 has general speed improvements. In particular, the processing time required for small indel runs is improved, due to parallelization within the module. Processing time comparisons with the 2.0 release are shown in the following table:

In addtion, feature requests and bug fixes have been incorporated based on the feedback from our customers.

To learn more, visit http://www.lifetechnologies.com/lifescope

Follow this unofficial tutorial step by step to run xsq-tools on CentOS Linux 6 and OpenSuse Linux

1. If you are using CentOS Linux 6, OpenSuse Linux check which GNU LIBC version you have   installed. you can use this command

[jacob@localhost xsq-tools]$ ldd – -version
ldd (GNU libc) 2.12
Copyright (C) 2010 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
Written by Roland McGrath and Ulrich Drepper.

to run xsq-tools you will need GNU libc version 2.5+ !!

2. Get Official xsq-tools software package distribution from LifeTech/ABI

3. Open your favorite Linux console command and untar/unzip xsq-tools package.

4. If you want to convert your xsq solid 5500 output files you will need to use the convertFromXSQ.sh shell script.

5. Backup the official convertFromXSQ.sh shell script.

6. Download unofficial convertFromXSQ.sh shell script and save in your official xsq-tools directory,

7. Download script from here

8. Rename script convertFromXSQ.sh1.txt to convertFromXSQ.sh

9. Set execution permission to shell script ( chmod +x convertFromXSQ.sh )

10. Change to xsq-tools directory using linux command line (cd XSQ_Tools)

11. Extras: You can run unofficial convertFromXSQ.sh shell script like this samples

$ remember, cd XSQ-Tools first

(A) $ ./convertFromXSQ.sh -c data/Frag.xsq

(B, from any location)

$ convertFromXSQ.sh -c /home/jacob/XSQ-Tools/data/Frag.xsq -o $HOME

(C) $ ./convertFromXSQ.sh – -rootXSQ `pwd` -c data/Frag.xsq (- – rootXSQ paramater is useful to myXSQ application)

That’s all.

Notes:

[1] If you do not set XSQ_TOOLS environment variable, the  unofficial convertFromXSQ.sh set XSQ_TOOLS environment variable  automatically.

[2] Follow LifeTech/ABI official readme documentation to install xsq-tools, the   unofficial script updates your .bashrc ,bash_profile in user home   directories but I disabled exporting PATH environment variable because I   need to do some tests, if you want to run unofficial convertFromXSQ.sh from any   location you will need to update and export your PATH environment   variable manually.

I hope that help you , it works for me !!

We want to see the SOLiD Community in Marco Island Florida for AGBT in February.  The deadline for submitting abstracts is Friday Oct 28^th, so don’t delay.  You can learn more at agbt.org/abstracts.html . 

Submit your abstract via email to papers@agbt.org  by Friday October 28, 2011.

Hope to see you in sunny Florida come February.

Have you ever wanted to perform de novo assembly using SOLiD type data? Now you can with HAPS.

HAPS (Hybrid Assembly Pipeline with SOLiD reads) is intended to assist customers to do de novo assembly using second-generation sequencing reads in a high-level manner. HAPS utilizes several modules with the functionalities of pre-processing, mapping, pairing, scaffolding, and gap-filling respectively. In other words, HAPS is a modularized scaffolder whose modules can be easily substituted so long as the Application Program Interface (API) specification is followed.

Figure 1:  Diagram showing the entire hybrid assembly workflow including HAPS

A typical hybrid assembly procedure using HAPS includes several stages:

1. An assembler (e.g. Velvet or others) is used to assemble various NGS (e.g., SOLiD, Ion Torrent, 454) reads or Sanger reads into contigs in FASTA format.

2. A mapping module is used to map SOLiD mate-pair (MP) reads onto these contigs.

3.A scaffolding module is then used to build scaffolds from the contigs using the mate-pair mapping results and finally a gap-filling module is used to fill intra-scaffold gaps.

The HAPS tool is available from http://solidsoftwaretools.com/gf/project/haps/ .

NOTE: This tool is not supported by Life Technologies. Any questions, issues, problems, bugs etc. must be submitted through the solidsoftwaretools.com developers website.