Category Archives: UNIX

A compilation of conversion tools for BED, SAM/BAM, psl, pslx, blast tabular and blast xml

A wide range of formats exist for representing the comparisons of different sequences to each other: blast tabular, blast xml, psl, pslx, SAM/BAM, BED
Most of these formats can be converted from one format to another. Sometimes the format is lossless allowing for the original data to be perfectly converted
without the loss of information. Other times, the format conversion is lossy permitting the conversion of only part of the original data resulting in the loss
of some information.

Here, I have compiled the tools or UNIX commands necessary for converting from one file format to another. As you can see, I am still needing to compete some of the gaps so please let me know of any other tools which are missing.

The command is shown in full below the table.

Conversion From Row/To Col blast-xml blast-tab psl pslx SAM/BAM BED
blast-xml N/A blast2tsv.xsl blastXmlToPsl blastXmlToPsl -pslx blast2bam BLAST_to_BED
blast-tab perl N/A ?? ?? ?? blast2bed
psl ?? ?? N/A pslToPslx pslToBed
pslx ?? ?? ?? N/A ?? pslToBed
SAM/BAM ?? ?? -s samtools view bedtools bamtobed
BED ?? ?? bed2psl ?? bedtools bedtobam N/A

Convert from SAM to psl using

Available from:

Example command:

This is a lossless format conversion with the -s option , however the sequence as a read is no longer supported in the psl format.


Convert from psl to SAM

Available from the samtools legacy scripts:

Example command:

This ends up being a lossy conversion as the read sequence is not in the output.


The options are used to calculate a blast like scoring see post:

Convert psl to pslx


This is a lossless conversion. For usage:


Convert SAM to fasta


Convert psl to BED

Option 1:

Using pslToBed from

This is a lossless conversion as the standard psl doesn’t have the sequence and so the bed file doesn’t either.



Option 2 as suggested by Alex Reynolds:

Using psl2bed from

This is also lossless when used with –keep-header:


As a bonus, it uses sort-bed to make a sorted BED file, so that it is ready to use with bedops, bedmap, etc.



Convert pslx to BED


This is a lossy conversion as the sequence is lost



Convert BAM to BED

Using bedtools:

This is a lossy conversion as the sequence data is lost.



Convert BED to BAM

Create the genome file for bed


Using the genome file and BED file to produce the BAM file.



The sequence is not present in the BED file so is absent from BAM as well. This is a lossy format conversion.
Additionally, there are differences in the number of read compared to the original file.

Convert BED to psl


This is a lossless conversion as neither the BED nor psl contain sequence information




Preparing blast-xml format

Preparing blast-tab format

Blast-xml to psl


This is a lossy conversion

However, if you use the -pslx option, you can get lossless conversion



Converting from blast-xml to SAM/BAM


This is a lossless conversion with sequence and read quality introduced.



Subsequently converted to BAM using samtools


Blast-xml to BED



This is a lossy conversion as the sequence information is lost.


Converting Blast tabular to blast-xml

Not completely possible due to missing information such as the alignment but see post
Or using the script from the blast2go google group:




Convert blast-xml to blast tabular

Several approaches have been suggested here but the most straight forward I have found is using the style sheet blast2tsv.xml from here: This is a lossless conversion but is not a standardly formatted blast-tabular output as it contains the sequence and the aligned site information in the last two columns.



Blast tabular to BED


The output will be in test.blasttab.bed, this is a lossless conversion neither blast-tabular nor BED have the sequence.



Converting SAM to BAM

Using samtools
This is a lossless format conversion.


Converting BAM to SAM using samtools

This is a lossless format conversion.


PS: I dedicate this tutorial to Sej, a great bioinformatician and friend 😉

Exploring the FAST5 format

FAST5 format from Oxford Nanopore (ONT) is in fact HDF5, which is a very flexible data model, library, and file format for storing and managing data. It is able to store an unlimited variety of datatypes.

A number of tools have been developed for handling HDF5 available from here.  The most useful are:

  • hdfview, a java visual tool for viewing HDF5 files with some limited functionality of plotting data and the option of exporting subsets in HDF5 (extension .h5)
  • h5ls, for listing specified entries of the HDF5 file
  • h5dump, to examine the HDF5 file and export specified groups or datasets in ASCII.

Here’s a run through exploring the lambda phage control run. First off, looking at the FAST5 file produced by the MinION.

At this stage, the FAST5 file only has one dataset which is the “Signal” dataset.

The same thing, on a FAST5 file, which has been processed by Metrichor, now has a lot more associated information, notably Fastq, Events, various Log files for the different analyses and still contains the raw Signal dataset.


To list all groups recursively using h5ls use -r:

Similar information can be obtained using h5dump -n:

To get all data and metadata for a given group /Raw/Reads/Read_939:

Or, the following is similar without the group tags. The -d option is used for printing a specified dataset.

Removing the array indices using option -y:

Saving the raw Signal dataset to file “test”:

The same as the above but specifying that the column width of the dataset is 1 with the option -w 1:

Dumping the whole FAST5 into XML format:

O.K., that it for now.

2nd Viral Bioinformatics and Genomics Training Course (1st – 5th August 2016)

We have shared our knowledge on Viral bioinformatics and genomics with yet another clever and friendly bunch of researchers. Sixteen delegates from across the world joined us for a week of intensive training. The line-up of instructors changed slightly due to the departure of Gavin Wilkie earlier in the year.

Joseph Hughes (Course Organiser)
Andrew Davison
Sejal Modha
Richard Orton (co-organiser)
Sreenu Vattipally
Ana Da Silva Filipe

The timetable changed a bit with more focus on advanced bash scripting (loops and conditions) as we asked the participants to have basic linux command experience (ls, mkdir, cp) which saved us a lot of time. Rick Smith-Unna’s Linux bootcamp was really useful for the students to check their expertise before the course:

The timetable this year follows and as in the previous year, we had plenty of time for discussion at lunch time and tea breaks and the traditional celebratory cake at the end of the week.

9:00-9:45                 Tea & Coffee in the Barn – Arrival of participants


The first day will start with an introduction to the various high-throughput sequencing (HTS) technologies available and the ways in which samples are prepared, with an emphasis on how this impacts the bioinformatic analyses. The rest of the first day and the second day will aim to familiarize participants with the command line and useful UNIX commands, in order to empower them to automate various analyses.

9:45-10:00           Welcome and introductions – Massimo Palmarini and Joseph Hughes
10:00-10:45        Next-generation sequencing technologies – Ana Da Silva Filipe
10:45-11:15        Examples of HTS data being used in virology – Richard Orton
11:15:11:30            Short break
11:30-11:45        Introduction to Linux and getting started – Sreenu Vattipally
11:45-12:30        Basic commands – Sreenu Vattipally
12:30-13:30            Lunch break in the Barn followed by a guided tour of the sequencing facility with Ana Da Silva Filipe
13:30-14:30        File editing in Linux – Sreenu Vattipally & Richard Orton
14:30-15:30        Text processing – Sreenu Vattipally & Richard Orton
15:30-16:00            Tea & Coffee in the Barn Room
16:00-17:30        Advanced Linux commands – Sreenu Vattipally

The second day will continue with practicing UNIX commands and learning how to run basic bioinformatic tools. By the end, participants will be able to analyse HTS data using various reference assemblers and will be able to automate the processing of multiple files.

9:30-11:00           BASH scripting (conditions and loops) – Sreenu Vattipally
11:00-11:30            Tea & Coffee in the Barn Room
11:30-12:15        Introduction to file formats (fasta, fastq, SAM, BAM, vcf) – Sreenu Vattipally & Richard Orton
12:15-13:00        Sequence quality checks – Sreenu Vattipally & Richard Orton
13:00-14:00            Lunch break in the Barn followed by a guided tour of the sequencing facility with Ana Da Silva Filipe
14:00-14:45        Introduction to assembly (BWA and Bowtie2)– Sreenu Vattipally & Richard Orton
14:45-15:30        More reference assembly (Novoalign, Tanoti and comparison of mapping methods) – Sreenu Vattipally & Sejal Modha
15:30-16:00            Tea & Coffee in the Barn Room
16:00-17:30        Post-processing of assemblies and visualization (working with Tablet and Ugene and consensus sequence generation) – Sreenu Vattipally & Sejal Modha

The third day will start with participants looking at variant calling and quasi-species characterisation. In the afternoon, we will use different approaches for de novo assembly and also provide hands-on experience.

9:30-11:00           Error detection and variant calling – Richard Orton
11:00-11:30            Tea & Coffee in Barn Room
11:30-13:00        Quasi-species characterisation – Richard Orton
13:00-14:00            Lunch break in the Lomond Room with an informal presentation of Pablo Murcia’s research program.
14:00-14:45        De novo assemblers – Sejal Modha
14:45-1:30           Using different de novo assemblers (e.g. idba-ud, MIRA, Abyss, Spades) – Sejal Modha
15:30-16:00            Tea & Coffee in the Barn
16:00-17:30        Assembly quality assessment, merging contigs, filling gaps in assemblies and correcting errors (e.g. QUAST, GARM, scaffold builder, ICORN2, spades) – Sejal Modha

On the fourth day, participants will look at their own assemblies in more detail, and will learn how to create a finished genome with gene annotations. A popular metagenomic pipeline will be presented, and participants will learn how to use it. In the afternoon, the participants will build their own metagenomic pipeline putting in practice the bash scripting learnt during the first two days.

9:30-10:15           Finishing and annotating genomes – Andrew Davison & Sejal Modha
10:15-11:00        Annotation transfer from related species – Joseph Hughes
11:00-11:30            Tea & Coffee in the Barn
11:30-12:15        The MetAMOS metagenomic pipeline – Sejal Modha & Sreenu Vattipally
13:00-14:00            Lunch break in Lomond Room with informal presentation of Roman Biek’s research program.
14:00-15:30        Practice in building a custom de novo pipeline – Sejal Modha & Sreenu Vattipally
15:30-16:00            Tea & Coffee in the Barn
16:00-17:30        Practice in building a custom de novo pipeline – Sejal Modha
17:30                         Group photo followed by social evening and Dinner at the Curler’s Rest ( 

On the final day, participants will combine the the consensus sequences generated during day two with data from Genbank to produce phylogenies. The practical aspects of automating phylogenetic analyses will be emphasised to reinforce the bash scripting learnt over the previous days.

9:30-10:15           Downloading data from GenBank using the command line – Joseph Hughes & Sejal Modha
10:15-11:00        Introduction to multiple sequence alignments – Joseph Hughes
11:00-11:30            Tea & Coffee in the Barn
11:30-1300         Introduction to phylogenetic analysis – Joseph Hughes
13:00-14:00            Lunch break in the Lomond Room with a celebratory cake
14:00-15:30        Analysing your own data or developing your own pipeline – Whole team available
15:30-16:00            Tea & Coffee in the Barn
16:00-17:00        Analysing your own data or developing your own pipeline – Whole team available
17:00                       Goodbyes
We wish all the participants lots of fun with their new bioinformatic skills.

If you are interested in finding out about future course that we will be running, please fill in the form with your details.

How to demultiplex Illumina data and generate fastq files using bcl2fastq

Sequence runs on NGS instruments are typically carried out with multiple samples pooled together. An index tag (also called a barcode) consisting of a unique sequence of between 6 and 12bp is added to each sample so that the sequence reads from different samples can be identified.

On the Illumina MiSeq, the process of demultiplexing (dividing your sequence reads into separate files for each index tag/sample) and generating the fastq data files required for downstream analysis is carried out automatically using the onboard PC. However, on the higher-throughput NextSeq500 and HiSeq models this process is carried out on BaseSpace – Illumina’s cloud-based resource.

Whilst there are many advantages to having your sequence data in the cloud (e.g. monitoring a sequence run from home, ease of sharing data with collaborators, etc) there are also some drawbacks to this system. In particular the process of demultiplexing and fastq file generation in BaseSpace can be very slow. It takes up to 8 hours to demultiplex the data from a high output NextSeq500 run on BaseSpace, and if the fastq files then have to be downloaded to your local computer or server for analysis this requires a further 3 hours.

If your data is urgent you may not want to wait 11 hours or more after your sequence run has finished to begin your analysis! We have found that demultiplexing and fastq file generation from a high output NextSeq500 run can instead be carried out in about 30 minutes on our in-house UNIX server. This also has the advantage of avoiding the rather slow step of downloading your fastq files from BaseSpace.

In order to do this, you need to install a free piece of software from Illumina called bcl2fastq on your UNIX server. Demultiplexing NextSeq500 data (or any Illumina system running RTA version 1.18.54 and later) requires bcl2fastq version 2.16 or newer (the latest version at the time of writing is v2.17 and can be downloaded here.

Importantly, we have checked that the results obtained from bcl2fastq and BaseSpace are equivalent – the fastq files generated are exactly the same. BaseSpace is set to remove adapter sequences by default, meaning that the sequence reads may not all be the same length (any reads from short fragments with adapter read-through will have those sequences removed). In bcl2fastq you have the option to either remove adapter sequences or leave them in so that all reads are the same length.

In order to demultiplex the data, first copy the entire run folder from the sequencer to your UNIX server. On the NextSeq500, the run folder will be inside the following directory on the hard disc –
D:\Illumina\NextSeq Control Software\Temp\
It ought to be the ONLY folder here as the NextSeq only retains data from the most recent run – as soon as you start a new sequence run the data from the previous run is deleted. Copy the entire folder, including all its subdirectories. This folder contains the raw basecall (bcl) files. Do not change the name of the folder, which will be named as per the following convention – YYMMDD_InstrumentID_RunID_FlowcellID
For example, the 10th run carried out on a NextSeq500 with serial number 500999, on 14th April 2016 and using flowcell number AHLFNLBGXX would be named as follows –

The other requirement is a sample sheet – a simple comma separated file (csv) with the library chemistry, sample names and the index tag used for each sample, in addition to some other metrics describing the run. Anyone running a MiSeq will already be familiar with these, but NextSeq and HiSeq users may only have used BaseSpace to enter these values. Unfortunately there is no way to automatically download a sample sheet from BaseSpace (although we have figured out a way round this to avoid double data entry, see the next blog post). Sample sheets can be made and modified using MS Excel or any other software that can read csv files, but the easiest way to make one is to use a free wizard-type program for the PC called Illumina Experiment Manager, which guides you through the process. The latest version at the time of writing is v1.9, which is available here.

Open Illumina Experiment Manager, and click on ‘Create Sample Sheet.’ Then, make certain that you choose the correct sequencer (essential since the NextSeq and MiSeq use opposite reverse complements during index reads). Select ‘Fastq only’ output. Enter any value (numbers or text) for the Reagent Kit Barcode – this will become the filename. Ensure correct library chemistry is selected (e.g. TruSeqLT, TruSeqHT, NexteraXT, etc). If there are custom/non-standard tags these will need to be manually entered in the csv file. Tick adapter trimming for read1 and read2 if required, select either paired or single end reads and enter the read length as appropriate (add one base, so for 150bp reads enter 151). Then either follow the instructions in the next blog post to import sample names and tags from BaseSpace, or enter them manually by adding a blank row for each sample, entering the sample names and selecting the index tag(s) for each sample. It is wise to double check that the sample names and indexes are correct, as mistakes will cause data to be allocated to the wrong file. Change the name of the file to ‘SampleSheet.csv’ and copy it into the top directory inside the sequence run folder on the server. The sample sheet file should resemble the example below – this is for a paired end 2x151bp NextSeq run with four samples, TruSeqLT index tags, and adapter trimming selected.


Now use the command line below on the server to run bcl2fastq. For speed, we use 12 threads for processing the data on our UNIX server (-p 12), however the optimal number will depend on your system architecture, resources and usage limits. It is important to set a limit to the number of threads, otherwise bcl2fastq will use 100% of the CPU’s on the server. We usually invoke the no-lane-splitting option, otherwise each output file from our NextSeq is divided into four (one for each lane on the flowcell). Here we are using the NextSeq run folder mentioned above as an example (160414_NB500999_0010_AHLFNLBGXX) and sending the output to a subdirectory within it called ‘fastq_files.’ For other bcl2fastq options please see Illumina’s manual on the software.

In this example, there should be two fastq files generated for each sample (one each for forward R1 and reverse R2 reads, since this is a paired end 2x151bp run) plus a forward and reverse file for ‘Undetermined’ reads where the index tag did not match any of the tags in the sample sheet. The Undetermined file will contain all of the reads from the PhiX spike-in if used (as PhiX does not have a tag) and also any other reads where there was a basecalling error during the index read. Depending on the PhiX spike-in % and the total number of samples on the run, the size of the Undetermined file should normally be smaller than the other files. If there is a problem suspected with demultiplexing or tagging always check the ‘index.html’ file within the ‘Reports/html’ subdirectory. This file will open on a standard web browser, and clicking the ‘unknown barcode’ option will display the top unknown barcodes and allow problems to be diagnosed. Common issues are that one or more samples were omitted from the sample sheet, errors entering the barcodes, incorrect library chemistry (e.g. selecting NexteraXT instead of TruSeqHT) or that the barcodes (especially sometimes index 2 on dual-indexed samples) need to be reverse-complemented on the sample sheet.

NCBI Entrez Direct UNIX E-utilities

I use NCBI Entrez Direct UNIX E-utilities regularly for sequence and data retrieval from NCBI. These UNIX utils can be combined with any UNIX commands.

It is available to download from the NCBI website:

A few useful examples for NCBI edirect utilities.

Download a sequence in fasta format from NCBI using accession number

Batch retrieval for all proteins for taxon ID. This example will download all proteins for viruses in fasta format.

Download sequences infasta format from NCBI using edirect using isolate info

Download sequences from NCBI using edirect using bioproject accession or ID

Get all CDS from a genome

Get taxonomy ID from protein accession number

Get taxonomy ID from accession number using esummary

Get full lineage from accession number
Tip : xtract can be used to fetch any element from the xml output

Get scientific name from accession number

Download all refseq protein sequences for viruses

Download reference genome sequence from taxonomy ID
Note: Using efilter command

Get all proteins from a genome accession

Extract genome accession from protein accession – DBSOURCE attribute in genbank file and an alternative to the script mentioned in one of my earlier blog post.
Note: Following command would work with protein accession and GIs used as -id parameter in elink command.

More info about NCBI Entrez Direct E-utillities is available on the NCBI website.

Bioinformatician at CVR.

How to make a BioLinux Live USB Stick – with persistent data storage

These are the steps I used to create a batch of bootable BioLinux Live USB sticks – with persistent data so that any data files created/downloaded would be preserved. This was used for a course so that each stick had the same NGS data and the same additional (non-BioLinux) programs pre-installed and already configured.

Step 1 – Download the BioLinux ISO file for use with DVD/USB media

The downloaded .iso file is an archive file that contains the whole BioLinux operating system – it can be used later to either install BioLinux onto a machine, or to create a bootable BioLinux USB Live disk. The bio-linux-8-latest.iso image is currently (March 2016) 3.58GB in size.

Step 2 – Download and install UNetbootin

UNetbootin allows you to create bootable Live USB drives for Ubuntu and other Linux distributions without burning a CD.

It is simple to install, on a Mac you just move the downloaded file into /Applications

Step 3 – Create an initial BioLinux Live USB disk with persistent data

As the .iso file is 3.58GB in size, a USB stick of atleast 4GB is needed, but that is a little to close for comfort, so best to go for a USB stick of atleast 8GB; these days 8GB sticks are very cheap (£2.99) and are the same price (if not cheaper) as 4GB sticks. To play safe, the USB stick should probably be in FAT32 format – FAT32 has a limitation of 4GB for file sizes – this includes the overall casper-rw BioLinux file which will be where all the persistent data is stored, so if you are going to be storing more than 4GB of data then you will probably need the NTFS file system on the USB stick.

Insert your blank USB key into your computer. Launch unetbootin. Select the “Diskimage” toggle button, select “ISO” from the drop down list, and then navigate to and select the BioLinux .iso file from your computer downloaded in Step 1. Next, in the field entitled “Space used to preserve files across reboots (Ubuntu only)” enter “3500” into the MB textfield (3.5 GB) – you could increase this above 4GB if you have a bigger USB stick and if it is using the NTFS file system. Next, select “USB Drive” from the “Type” drop down list, and then select your actual USB stick from the “Drive” drop down list and then click “OK” to create your bootable BioLinux Live USB stick with persistent data storage.

Step 4 – Boot into your BioLinux

Next step is to boot into the BioLinux Live USB disk from a machine – this will need to be a Windows or Linux machine, a modern Mac is unlikely to boot up from it. Turn the computer off, insert the BioLinux Live USB stick into the computer, turn the computer back on, and get ready. As soon as the first screen appears – which normally has the computer manufacturer logo – it should say something like “Press F12 to Choose Boot Device” at the bottom of the screen – so press F12 quickly before the screen disappears. Sometimes it is not F12, sometimes it is F10 or F2 or another key, but it should say on the screen what button to press. This will launch the BIOS menu. Enter the “Boot Device Select” menu, and move your USB Stick up the boot order to the top, so that the computer will now boot from the USB stick before its own hard drive. Exit the BIOS menu, saving any changes, and the computer should now boot into the BioLinux Live USB stick.

Step 5 – Customise your BioLinux – add data and programs

Now you will be inside your own BioLinux OS on the USB stick. So install any extra programs you want, configure PATHs, and download any data files you want. The programs, configs and data will be saved onto the USB stick and preserved – due to the persistent data storage and the casper-rw file.

Now shutdown BioLinux, remove the USB stick, and boot back into your normal operating system.

Step 6 – Make an image copy of your customised BioLinux disk

Once inside your normal operating system, insert the BioLinux USB stick back in. The next step will only work on a Mac or a Linux machine as it using the dd command.

This copies the BioLinux Live USB stick (located at /dev/disk2 on my machine – on a mac run “diskutil list” to see where yours is) and it creates a single biolinux.img file in the Documents folder which contains the entire operating system along with all the extra data and programs I installed.

The original customised BioLinux Live USB stick can now be ejected and removed.

Step 7 – Copy Copy Copy

Insert a new blank USB stick into the computer (obviously it needs to be atleast the same size as the original one). Now we want to make a copy of that original BioLinux Live USB stick onto the new USB stick using the dd command:

This copies the biolinux.img file located in the Documents folder that we created in Step 6, onto the new blank USB disk (located at /dev/disk2 – check where yours is). On a Mac, I had to first go into DiskUtility and dismount the FAT32 partition of the USB stick before dd would work – not dismount the USB stick itself, just the FAT32 partition. The key thing here, is that you can insert multiple blank USB sticks into all the available USB sticks and run the dd command in parallel:

For an 8GB USB stick, this copying process took almost exactly 1 hour. Then you can eject the USB sticks and put new ones in and copy another batch.


Many thanks to Paul Capewell and Willie Weir for a tip on the dd command.

Submitting a job to run on another server and retrieving the results

Imagine having two different servers called darwin and linnaeus. Imagine that darwin is a great server with loads of RAM for doing de-novo assembly and that linnaeus has loads of nodes so a great server for splitting up jobs and running lots of jobs in parallel. To make good use of all these resources, it would make sense to do part of the processing on one server and then automatically send jobs to be processed on another server.

So this is how you do that. On linnaeus you run:

You copy the key and on darwin put the key in .ssh/authorized_keys2

The reverse also needs to be done by putting a darwin key on linnaeus.

Now to test it out create the shell script that will be executed on linnaeus e.g. linnaeusshell:

This small script will uncompress a file, return the uncompressed file and return a “Done” log to darwin once the script is finished.

Now create a command shell on darwin, e.g. darwinshell:

And finally execute the darwinshell:

This will transfer the Pf3D7_01.embl.gz compressed file over to linnaeus where the file will be uncompressed and transferred back to darwin.

Big thanks to Sreenu who helped me a lot to sort this out.

Top tips to keep your home folder on a server tidy

All bioinformatics server users and administrators would know how easy it is to fill up our home directories with huge amounts of data, especially when you are analysing deep sequencing data on a daily basis.

Here is a list of a few useful commands and tips that can help to keep your home directory tidy.

  • Do not copy fastq files to multiple locations, create soft links instead using the following command in your working directories.

  • Always convert .sam files to .bam files

  • Zip any data files/folders that are not going to be used for next few weeks.

  • To compress a directory and all the data within it run the following command

  • Organise your home directory well.

Keep all reference sequences in one folder

Keep all indexes in one folder (this could be the same folder as the references for simplicity)

  • Always delete temporary and intermediate files and keep a log of deleted files in a text file.
  • Empty the trash folder if you use a GUI or Virtual Desktop Environment
  • Use ncdu, tree or baobab (GUI) commands to find out disk consumption

  • Find out the size of your home directory using the following du command

  • For advanced users:

As mentioned in this stackoverflow forum, if you would like to get a list of multiple copies of files in your directory use the following set of commands.


Bioinformatician at CVR.

1st Viral Bioinformatics and Genomics Training Course


The first Viral Bioinformatics and Genomics training course held at the University of Glasgow was completed successfully by 14 delegates (nine external and five internal) on 10-14 August 2015. The course took place in the McCall Building computer cluster, and the adjacent Lomond and Dumgoyne Rooms were used for refreshments and lunch.

Joseph Hughes (Course Organiser)
Andrew Davison
Robert Gifford
Sejal Modha
Richard Orton
Sreenu Vattipally
Gavin Wilkie

9:00-10:00 Tea & Coffee in Dumgoyne Room – Arrival of participants

Day one will introduced the participant to the different sequencing technologies available, the ways in which samples are prepared with an emphasis on how this impacts the bioinformatic analyses. The rest of the day aimed to familiarize the researcher with the command line and useful UNIX commands.


10:00-10:45 Next-generation sequencing technologies – Gavin Wilkie
10:45-11:30 Examples of HTS data being used in virology – Richard Orton
11:30-11:45 Brief introduction and history of Unix/Linux – Sreenu Vattipally and Richard Orton
11:45-12:30 The command line anatomy and basic commands – Sreenu Vattipally and Richard Orton

12:30-13:30 Lunch break in Dumgoyne Room

13:30-14:30 Essential UNIX commands – Sreenu Vattipally and Sejal Modha
14:30-15:30 Text processing with grep – Sreenu Vattipally and Sejal Modha
15:30-16:00 Tea & Coffee in Dumgoyne Room
16:00-17:30 sed and awk – Sreenu Vattipally and Sejal Modha


The participant continued to practice UNIX commands and learn how to run basic bioinformatic tools. By the end of the day, the participant were able to analyse HTS data with different reference assemblers and automate the processing of multiple files.

9:30-10:15 Introduction to file formats (fasta, fastq, SAM, BAM, vcf) – Sreenu Vattipally and Richard Orton
10:15-11:00 Quality scores, quality control and trimming (Prinseq and FastQC and trim_galore) – Sreenu Vattipally and Richard Orton
11:00-11:30 Tea & Coffee in Dumgoyne Room
11:30-13:00 Introduction to alignment and assembly programs (Blast, BWA, Bowtie) – Sreenu Vattipally and Richard Orton

13:00-14:00 Lunch break in Dumgoyne Room

14:00-14:45 Continuation of alignment and assembly programs (Novoaligner, Tanoti) – Sreenu Vattipally and Richard Orton
14:45-15:30 Post-assembly processing and alignment visualization (Tablet and UGENE) – Sreenu Vattipally and Richard Orton
15:30-16:00 Tea & Coffee in Dumgoyne Room
16:00-16:45 Workflow design and automation – Sreenu Vattipally and Richard Orton
16:45-17:30 Loops and command line arguments – Sreenu Vattipally and Richard Orton


The third day covered the different approaches used for de-novo assembly and provided hands-on experience. A popular metagenomic pipeline was presented and participants learned how to use it as well as create their own pipeline. (Slides and practical)


9:30-10:15 De-novo assemblers – Sejal Modha and Gavin Wilkie
10:15-11:00 Using different de-novo assemblers (e.g. Meta-idba, Edena, Abyss, Spades) – Sejal Modha and Gavin Wilkie

11:00-11:30 – Tea & Coffee in Lomond Room
11:30-13:00 Scaffolding contigs, filling gaps in assemblies and correcting errors (e.g. phrap, gap_filler, scaffold builder, ICORN2, spades) – Gavin Wilkie and Sejal Modha

13:00-14:00 Lunch break in Lomond Room

14:00-14:45 Practice building a custom de-novo pipeline (BLAST, KronaTools)
14:45-15:30 the MetAmos metagenomic pipeline – Sejal Modha and Sreenu Vattipally
15:30-16:00 Tea & Coffee in Lomond Room
16:00-17:30 Analysis using the pipeline – Sejal Modha and Sreenu Vattipally


Day four gave the participant the opportunity to look into more detail at their assembly, they learned how to create a finished curated full genome (emphasis on finishing) with gene annotations and analysed the variation within their sample

9:30-11:00 Finishing and Annotating genomes – Andrew Davison, Gavin Wilkie and Sreenu Vattipally
11:00-11:30 Tea & Coffee in Dumgoyne Room
11:30-13:00 Annotation transfer from related species – Gavin Wilkie, Andrew Davison and Sreenu Vattipally

13:00-14:00 Lunch break in Dumgoyne Room


14:00-15:30 Error detection and variant calling – Richard Orton
15:30-16:00 Tea & Coffee in Dumgoyne Room
16:00-17:30 Quasi-species characterisation – Richard Orton

17:30 Social evening in the pub/restaurant at Curlers on Byres Road organised by Richard Orton ( 



Researchers worked through several practical examples of using sequences in virology and spent the remaining time analysing their own data with the teachers help.

9:30-10:15 Identifying mutations of interest for individual sequences and within a set of sequences – Robert Gifford
10:15-11:00 Combining phylogenies with traits – Robert Gifford
11:00-11:30 Tea & Coffee in Dumgoyne Room
11:30-12:15 Investigating epidemiology (IDU example) – Robert Gifford
12:15-13:00 Investigate transmission of drug resistance – Robert Gifford

13:00-14:00 Lunch break in Dumgoyne Room

14:00-15:30 Analysing your own data or developing your own pipeline
15:30-16:00 Tea & Coffee in Dumgoyne Room
16:00-17:00 Analysing your own data or developing your own pipeline
17:00 Goodbyes



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Setting up automatic BLAST database update on linux servers

Basic Local Alignment Search Tool (BLAST) is one of the most commonly used programs for sequence classification using similarity search.

Standalone BLAST can be setup easily on the local server. More info about how to set it up on a local Linux server can be found here:

In our lab, all our servers run the BioLinux operating system and BLAST is pre-installed on the server. With local BLAST, it is important to update local BLAST databases regularly to include new sequences submitted to NCBI. However, sometimes it does become a bit tricky to install and regularly update these databases.

Here is a small tutorial about how to setup local BLAST databases and regularly update them.

In BioLinux, the BLASTDB variable path is usually set up to /var/lib/blastdb and is specified in the file in /etc/profile.d/

The standard file looks like this.

BLASTDB path can be updated to /your/blastdb/location by changing details in the “if” statement of the file.

The following example shows how I will change the location to my customized blastdb in my home directory /home/sejalmodha/blastdb

On a standard linux server you can specify the BLASTDB path variable in /etc/bash.bashrc or in your local ~/.bashrc

To update these databases regularly on the server, use NCBI’s update_blastdb script and wrap it in a cronjob.

I have an script that downloads nr, nt and refseq_protein databases from the NCBI website and changes the permissions of those files so that all users can use the files.

To schedule the downloading of these databases monthly, put it in a cronjob called blast_cronrun and save the log to download.log file.

The last step is to submit the cronjob using the crontab command.


Bioinformatician at CVR.