PAKAP - Revision history

Uqccha18 at 07:07, 2 May 2018

2018-05-02T07:07:31Z

2014-07-02T06:35:37Z

‎Download

2014-06-30T04:12:34Z

‎Download

2014-06-30T04:12:26Z

‎Download

2014-04-30T06:09:34Z

‎How to interpret the results

2014-04-29T03:14:23Z

‎How to interpret the results

2014-04-29T02:08:53Z

‎How to install

2014-04-29T02:08:19Z

‎Download

2014-04-29T02:08:09Z

‎Download

2014-04-29T01:58:01Z

‎Download

← Older revision		Revision as of 07:07, 2 May 2018
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−	INTRODUCTION ~~(from paper?)~~	+	INTRODUCTION

		+	Presence/Absence variants (PAVs) have recently been shown to be abundant in plant and fungi genomes. Genome wide PAV detection is usually performed during the construction of pangenomes for species, using either the whole genome assembly and comparison or the iterative mapping and assembly approach. These rely on the presence of at least one reference genome, and can be confounded by variations in assembly and annotation quality.

	We have established a Present/Absent Kmer Analysis Pipeline (PAKAP). By reducing each read to component k-mers and comparing the relative abundance of these sub-sequences, we overcome statistical limitations of whole read comparative analysis.		We have established a Present/Absent Kmer Analysis Pipeline (PAKAP). By reducing each read to component k-mers and comparing the relative abundance of these sub-sequences, we overcome statistical limitations of whole read comparative analysis.

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 == Download ==
-* Latest Version 1.6:
+* Latest Version 1.7:
-** [http://appliedbioinformatics.com.au/download/PAKAP_1.6.zip PAKAP_1.6.zip]
+** [http://appliedbioinformatics.com.au/download/PAKAP_1.7.zip PAKAP_1.7.zip]
 * Example data (244MB)

@@ Line 23: / Line 23: @@
 == Download ==
-* Latest Version 1.5:
+* Latest Version 1.6:
 ** [http://appliedbioinformatics.com.au/download/PAKAP_1.6.zip PAKAP_1.6.zip]

@@ Line 24: / Line 24: @@
 == Download ==
 * Latest Version 1.5:
-** [http://appliedbioinformatics.com.au/download/PAKAP_1.5.zip PAKAP_1.5.zip]
+** [http://appliedbioinformatics.com.au/download/PAKAP_1.6.zip PAKAP_1.6.zip]
 * Example data (244MB)

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 . PARs - this folder contains the reads present only in one of the two conditions:
-.1 ''s1_0_PARs_A.fasta'' - the first partner reads of the pairs only present in s1
+.1 ''s1_0_PARs_single.fasta'' - reads only present in s1
-.2 ''s1_0_PARs_B.fasta'' - the second partner reads of the pairs only present in s1
+.2 ''s2_0_PARs_single.fasta'' - reads only present in s2
-.3 ''s1_0_PARs_single.fasta'' - the pairs, together with unpaired reads only present in s1
+. SOAPs - optional, only when reference is specified: The SOAP results for aligning the reads only present in s1 or s2. Can be converted to SAM/BAM using SOAPaligner's soap2sam.pl and samtools view.
 . Results - optional, only when gff file and reference are specified: A table detailing how many genes there are in the given gff-file, how many reads were produced by the pipeline, how many reads could be aligned, how many reads hit inside the genes in the gff3 file, how many reads hit only with their start or their end inside genes, how many reads hit multiple times etc. Conditions that have no reads produced (like in the example data) are listed like with '-'. Example:

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 . SOAPs - optional, only when reference is specified: The SOAP results for aligning the paired and single reads from s1 and s2. Can be converted to SAM/BAM using SOAPaligner's soap2sam.pl and samtools view.
-. Results - optional, only when gff file and reference are specified: A table detailing how many genes there are in the given gff-file, how many reads were produced by the pipeline, how many reads could be aligned, how many reads hit inside the genes in the gff3 file, how many reads hit only with their start or their end inside genes, how many reads hit multiple times etc. Example:
+. Results - optional, only when gff file and reference are specified: A table detailing how many genes there are in the given gff-file, how many reads were produced by the pipeline, how many reads could be aligned, how many reads hit inside the genes in the gff3 file, how many reads hit only with their start or their end inside genes, how many reads hit multiple times etc. Conditions that have no reads produced (like in the example data) are listed like with '-'. Example:
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 |0
 |0
 |}
 Here, the large majority of produced reads aligns completely inside the given 232 genes (2655 out of 2670 reads). 8 reads align only with their start and then stretch outside the gene, 7 reads start before the gene and then end inside the gene. 0 of the reads align somewhere else, and all reads align uniquely.

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 == How to install ==
-* Download the [link_here DiffKAP package].
+* Download the package from above, then just extract the files to a directory of your choosing. Remember the directory, since you'll need that in the next step.
 == How to prepare the pipeline ==

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 * Example data (244MB)
-** [http://appliedbioinformatics.com.au/download/PAKAP_example_data.zip PAKAP_example_data.zip
+** [http://appliedbioinformatics.com.au/download/PAKAP_example_data.zip PAKAP_example_data.zip]
 == How to install ==