The contigs are sometimes then ordered and oriented in relation to one another to form scaffolds. The distances between pairs of a set of paired end reads is useful information for this purpose. The mechanisms used by assembly software are varied but the most common type for short reads is assembly by de Bruijn graph. Genome assembly is a very difficult computational problem, made more difficult because many genomes contain large numbers of identical sequences, known as repeats.
These repeats can be thousands of nucleotides long, and some occur in thousands of different locations, especially in the large genomes of plants and animals. Determining the DNA sequence of an organism is useful in fundamental research into why and how they live, as well as in applied subjects. Because of the importance of DNA to living things, knowledge of a DNA sequence may be useful in practically any biological research. For example, in medicine it can be used to identify, diagnose and potentially develop treatments for genetic diseases.
Similarly, research into pathogens may lead to treatments for contagious diseases [2]. Raw read sequences can be stored in a variety of formats. The reads can be stored as text in a Fasta file or with their qualities as a FastQ file.
They can also be stored as alignments to references in other formats such as SAM or its binary compressed implementation BAM. All of the file formats with the exception of the binary BAM format can be compressed easily and often are stored so.
The most common read file format is FastQ as this is what is produced by the Illumina sequencing pipeline.
This will be the focus of our discussion henceforth. There are a number of tools available for each step in the genome assembly protocol. These tools all have strengths and weaknesses and have their own application space.
Suggestions rather than prescriptions for tools will be made for each of the steps. Other tools could be substituted in each case depending on user preference, experience or problem type. Depending on your requirements and skill base there are two options for running this protocol using GVL computing resources.
You can use Galaxy-tut or your own GVL server. The purpose of this section of the protocol is to show you how to understand your raw data, make informed decisions on how to handle it and maximise your chances of getting a good quality assembly.
Knowledge of the read types, the number of reads, their GC content, possible contamination and other issues are important. Cleaning up the raw data before assembly can lead to much better assemblies as contamination and low quality error prone reads will have been removed.
It will also give you a better guide as to setting appropriate input parameters for the assembly software. It is a good idea to perform these steps on all of your read files as they could have very different qualities. Details can be found here. FastQC can be run from within Galaxy or by command line. It has a GUI interface for the command line version.
Now that you have some knowledge about the raw data, it is important to use this information to clean up and trim the reads to improve its overall quality before assembly. This maintains the paired ordering of the reads in the paired read files so the assembly software can use them correctly.
The suggested tool for this is a pair aware read trimmer called Trimmomatic. Details on Trimmomatic can be found here. Command line: details and examples here. It only produces 1 output read file if you used it in single ended mode. Each read library 2 paired files or 1 single ended file should be trimmed separately with parameters dependent on their own FastQC reports.
The output files are the ones you should use for assembly. Read quality trimming: nesoni clip , part of the nesoni suite of bioinformatics tools. The purpose of this section of the protocol is to outline the process of assembling the quality trimmed reads into draft contigs. Most assembly software has a number of input parameters which need to be set prior to running. These parameters can and do have a large effect on the outcome of any assembly.
Assemblies can be produced which have less gaps, less or no mis-assemblies, less errors by tweaking the input parameters. Therefore, knowledge of the parameters and their effects is essential to getting good assemblies. In most cases an optimum set of parameters for your data can be found using an iterative method. The suggested assembly software for this protocol is the Velvet Optimiser which wraps the Velvet Assembler. The Velvet assembler is a short read assembler specifically written for Illumina style reads.
It uses the de Bruijn graph approach see here for details. Velvet and therefore the Velvet Optimiser is capable of taking multiple read files in different formats and types single ended, paired end, mate pair simultaneously. The quality of contigs that Velvet outputs is dependent heavily on its parameter settings, and significantly better assemblies can be had by choosing them appropriately.
Velvet Optimiser is a Velvet wrapper that optimises the values for the input parameters in a fast, easy to use and automatic manner for all datasets.
It can be run from within GVL Galaxy servers or by command line. The critical inputs for Velvet Optimiser are the read files and the k-mer size search range. The read files need to be supplied in a specific order.
Single ended reads first, then by increasing paired end insert size. The k-mer size search range needs a start and end value. If you set the start hash size to be higher than the length of any of the reads in the read files then those reads will be left out of the assembly.
The output from FastQC can be a very good tool for determining appropriate start and end of the k-mer size search range. The per base sequence quality graph from FastQC shows where the quality of the reads starts to drop off and going just a bit higher can be a good end value for the k-mer size search range.
The Velvet Optimiser log file contains information about all of the assemblies ran in the optimisation process. At the end of this file is a lot of information regarding the final assembly. This includes some metric data about the draft contigs n50, maximum length, number of contigs etc as well as the estimates of the insert lengths for each paired end data set.
It also contains information on where to find the final contigs. The assembly parameters used in the final assembly can also be found as part of the last entry in the log file.
More detailed metrics on the contigs can be gotten using a fasta statistics tool such as fasta-stats on Galaxy. Assembly: There are a large number of short read assemblers available. Each with their own strengths and weaknesses. Basically, hexadecimal number system represents a binary data by dividing each byte in half and expressing the value of each half-byte. To convert a binary number to its hexadecimal equivalent, break it into groups of 4 consecutive groups each, starting from the right, and write those groups over the corresponding digits of the hexadecimal number.
To convert a hexadecimal number to binary, just write each hexadecimal digit into its 4-digit binary equivalent. A negative binary value is expressed in two's complement notation. According to this rule, to convert a binary number to its negative value is to reverse its bit values and add 1. To subtract one value from another, convert the number being subtracted to two's complement format and add the numbers. The process through which the processor controls the execution of instructions is referred as the fetch-decode-execute cycle or the execution cycle.
The processor may access one or more bytes of memory at a time. Let us consider a hexadecimal number H. This number will require two bytes of memory. The high-order byte or most significant byte is 07 and the low-order byte is The processor stores data in reverse-byte sequence, i. So, if the processor brings the value H from register to memory, it will transfer 25 first to the lower memory address and 07 to the next memory address.
When the processor gets the numeric data from memory to register, it again reverses the bytes. Frahaan Hussain. Assembly - Introduction Advertisements. Previous Page. Next Page. Useful Video Courses. More Detail. Previous Page Print Page.
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