GenePix Array List (GAL) files are text files with specific information about the location, size, and name of each DNA spot on a microarray. They are therefor of vital importance for the analysis of scanned microarray images. The format defines a specific header before the list of data columns follows:

Example:

ATF	1			

9	5			

Type=GenePix ArrayList V1.0				

BlockCount=1				

BlockType=0				

"Block1=10000, 38780, 150, 20, 200, 18, 200"				

Supplier=BioRobotics				

ArrayerSoftwareName=TAS Application Suite (MicroGrid II)				

ArrayerSoftwareVersion=2.7.1.18					

ScanResolution=10	

Block	Column	Row	ID	Name

1	1	1	RP11-163J21	Clone 1

1	1	2	RP11-163J21	Clone 2

Explanantions:

ATF -> File conforms to Axon Text File
1 -> Version number of ATF
9 -> Number of header lines before the "Block, Column, Row, ..." line
5 -> Number of data columns (Block, Column, Row, Name, ID)
Type=GenePix ArrayList V1.0 -> Type of file, same for all GAL files
Block Count=1 -> Number of blocks described in the file
Block Type=0 -> Type of block, 0 = rectangular Block
X=A, B, C, D, E, F, G -> The position and dimensions of each block.
A -> xOrigin
B -> yOrigin
C -> Feature diameter
D -> xFeatures
E -> xSpacing
F -> yFeatures
G -> ySpacing ScanResolution - Optional parameter to scale the position on higher-resolution images Block arrangement

1	2	3	4

5	6	7	8

9	10	11	12

13	14	15	16

The data columns are:

• Block
• Column
• Row
• Name
• ID

Further reading and sources:

• Axon
• Molecular Devices
• See also: Microarray Scanners and PGS consulting in the UK & Ireland

Congress passed the Clinical Laboratory Improvement Amendments (CLIA) act in 1988 to establish quality standards for all non-research laboratory testing:

1. Performed on specimens derived from humans; and
2. For providing information for the diagnosis, prevention, and treatment of disease or impairment, or assessment of health.

The objective of the CLIA  program is to ensure quality in laboratory testing procedures and specifically to establish quality standards to ensure the accuracy, reliability, and timeliness of the patient’s test results. The CLIA Quality System Regulations became effective on April 24, 2003. Now the laboratory is required to check (verify) the manufacturer's performance specifications provided in the package insert for:

• Accuracy: If test results for previously tested samples fall within the stated acceptable limits, accuracy is verified
• Precision: Can the results be repeated mulitple times on the same day and on different days by different operators.
• Reportable range: Use known samples to confirm the upper and lower limits of the test.
• Also: Reference range or interval: Do the reference ranges provided by the test system's manufacturer fit your patient population?

The number of samples needs to be established for every test, 20 samples are seen as a "rule of thumb".

The FDA defines a Laboratory Developed Test (LDT) as an in vitro diagnostic test that is manufactured by and used within a single laboratory (i.e. a laboratory with a single CLIA certificate). LDTs are also sometimes called in-house developed tests, or “home brew” tests. Similar to other in vitro diagnostic tests, LDTs are considered “devices,” as defined by the FFDCA, and are therefore subject to regulatory oversight by FDA.

Sources:Centers for Medicare & Medicaid Services, Genohub

Most of us take vaccinations for granten and rely on them from our very first days. The whooping cough as an example can be deadly, especially for young babies who are too young to be protected by their own vaccination. Since 2010, the Centers for Disease Control and Prevention (CDC) has recorded between 10,000 and 50,000 cases each year in the United States and up to 20 babies dying. One recent study showed that many whooping cough deaths among babies could be prevented if all babies received the first dose of vaccination on time at 2 months old, when they are old enough to get vaccinated (CDC). Still, some parents believe they know better and risk their childrens life by not vaccinating them at all. 

For the US the CDC recommends vaccination of newborns / babies against the following diseases:

• Haemophilus influenzae type b (Hib)
• Diphtheria
• Hepatitis A
• Hepatitis B
• Influenza
• Measles
• Mumps
• Pertussis (whooping cough)
• Pneumococcal disease
• Polio
• Rubella (German measles)
• Tetanus (lockjaw)
• Rotavirus
• Varicella (chickenpox)

For Germany the situation is almost the same and the following vacciantions are recommended for babies under 2 years:

• Hib H. influenzae Typ b
• Diphtherie
• Hepatitis B
• Masern
• Mumps
• Pertussis (Keuchhusten)
• Pneumokokken
• Poliomyelitis (Kinderlaehmung)
• Röteln
• Tetanus
• Rotaviren
• Varizellen (Windpocken)
• Meningokokken C

Sources: CDC, Robert-Koch-Institut

As part of the Primary Analysis Illumina sequencing machines measure the intensity of the channels used for encoding the different bases and identify the most likely base at a given position of a sequencing read (tag). The Real Time Analysis (RTA) software writes the base and the confidence in the call as a quality score to base call (.bcl) files. As the name implies this is done in real time, i.e. for every cycle of the sequencing run a call for every location identified on the flow cell (tiles and lanes) is added. Bcl files are stored in binary format and represent the raw data output of a sequencing run. The format is described here. Software such as Casava/BclToFastq, Eland or the iSAAC aligner can make use of these files.

The *.bcl files are stored in the BaseCalls directory:

<run directory>/Data/Intensities/BaseCalls/L<lane>/C<cycle>.1

They are named in the format:

s_<lane>_<tile>.bcl

If you want to overcome errors during downstream processing from missing calls, software such as iSAAC and configureBclToFastq have an "--ignore-missing-bcl" command line option. This will interpret missing *.bcl files as no call (N) at that position.

Sources: Illumina, SeqAnswers

Cystic Fibrosis, also called Mucoviscidos, is a hereditary disease (autosomal recessive) in which exocrine (secretory) glands produce abnormally thick mucus. This mucus can cause problems in digestion, breathing, and body cooling. It affects up to one out of 3000 newborns (with northern European ancestry). There are well over a hundred genetic changes linked to CF. It is an area companies like Illumina are very active in with a special assay cleared as an in-vitro diagnostic test with the FDA for the detection of most of the genetic variants known to cause the disease.

Here are notes from a presentation Dr. Carlos Bustamante gave at a recent ClinGen conference:

Background for CF and CFTR

CFTR:

• Cysstic Fibrosis Transmembrane Conductance Regulator
• ABC transporter (ATP-binding cassette), that functons as ion channel
• cAMP-regulated through R domain phosphorylaton
• Transports chloride and thiocyanate across epithelial cell membranes
• 1,480 amino acids 

CF disease:

• Most common autosomal recessive disorder among Caucasians (1/3,300)
• Dysregulaton of epithelial fluid transport in lung, pancreas, and other organs
• ~ 2,000 identfied gene mutatons
• Phe508del – most common, in 70% cases
• Wide range of severity, most die of pulmonary disease at mean age of 37

Variants:

• ~70% of variants currently classed as VUS (variant of unknown significance)
• ~65% are missense mutations, 24% frameshift & stop-gained, 9% synonymous

Testing Machine Learning Approaches for CF classification

• Machine learning algorithms show higher performance when compared with separate predictors
• Tree-based methods perform the best (GBM & RF AUC is 6% higher then the best predictor, MutPred)
• Top features: MutPred, AF, SIFT, CADD, POSE
• Predicted pathogenicity probability (RF.pred) correlates with available experimental data for Cl- conductance and sweat Cl-

 Other sources used: PubMedHealth, Wikipedia

CRAM files are compressed versions of BAM files containing (aligned) sequencing reads. They represent a further file size reduction for this type of data that is generated at ever increasing quantities. Where SAM files are human-readable text files optimized for short read storage, BAM files are their binary equivalent, and CRAM files are a restructured column-oriented binary container format for even more efficient storage.

Tke key components of the approach are that positions are encoded in a relative way (i.e., the difference between successive positions is stored rather than the absolute value) and stored as a Golomb code. Also, only differences to the reference genome are listed instead of the full sequence.

The compression rates achieved are shown in the graph below generated by Uppsala University:

Comparing speed: Using the C implementation of for CRAM (James K. Bonfield), decoding is 1.5–1.7× slower than generating BAM files, but 1.8–2.6× faster at encoding. (File size savings are reported at 34–55%.(

Additional compression can be achieved by reducing the granularity of the quality values which will result in lossy compression though. Illumina suggested a binning of Q scores without significant calling performance.

Binning of similar Q-scores (Illumina):



Compression achieved by Q-score binning (Illumina):

Sources and further reading:

1. Format definition and usage
2. cram-toolkit
3. Detailed report at the Uppsala University
4. SAMtools with CRAM support
5. Original article from Markus Hsi-Yang Fritz, Rasko Leinonen, Guy Cochrane and Ewan Birney
6. Article about the implementation in C
7. Illumina while paper on Qscore compression

Quality scoring of the base calls

"Quality scores measure the probability that a base is called incorrectly. With SBS technology, each base in a read is assigned a quality score by a phred-like algorithm, similar to that originally developed for Sanger sequencing experiments. The quality score of a given base, Q, is defined by the equation
Q = -10log10(e)
where e is the estimated probability of the base call being wrong. Thus, a higher quality score indicates a smaller probability of error."(1)
The quality score is usually quoted as QXX, where the XX is the score and refers to that a particular call (or a all base calls of a read / of a sample / of a run) has a probability of error of 10^(-XX/10). For example Q30 equates to an error rate of 1 in 1000, or 0.1%, Q40 equates to an error rate of 1 in 10,000 or 0.01%.

During the primary analysis (real-time analysis, RTA) on the sequencing machine, quality scoring is performed by calculating a set of predictors for each base call, and using those predictor values to look up the quality score in a quality table. The quality table is generated using a modification of the Phred algorithm on a calibration data set representative of run and sequence variability

"It is important to note how quickly or slowly quality scores degrade over the course of a read. With short-read sequencing, quality scores largely dictate the read length limits of different sequencing platforms. Thus, a longer read length specification suggests that the raw data from that platform have consistently higher quality scores across all bases." (1)

Mapping / Alignment scores

For each alignment, BWA calculates a mapping quality score, which is the (Phred-scaled) probability of the alignment being incorrect. The algorithm is similar between BWA and MAQ, except that BWA assumes that the true hit can always be found. The probability for every base is calculated as:

p = 10 ^ (-q/10)

where q is the quality. For example a mapping quality of 40: 10^-4 = 0.0001, which means there is a 0.01% chance that the base is aligned incorrectly.

Example for a whole read:

If your read is 25 bp long and the expected sequencing error rate is 1%, the probability of the read with 0 errors is:

0.99^25 = 0.78

If there is 1 perfect alignment and 5 possible alignment positions with 1 mismatch, we combine these probabilities: The probability of the read with 1 error is
0.20
combined posterior probability that the best alignment is correct:

P(0-errors) / (P(0-errors) + 5 * P(1-errors))

= 0.44, which is low.

Base quality is apparently not considered in evaluating hits in bwa.

Sources:

1. Illumina
2. BWA paper
3. DaveTang blog
4. jwfoley on SEQanswers
5. Ying Wei's notes
6. Gene-Test bioinformatics (PGS / NGS) consulting

To assess whether a new test (e.g. a diagnostic tests or medical device testing for disease or non-disease status) is equivalent to an existing test, the following measures can be reported. They can be of importance for the submission of premarket notification (510(k)) or premarket approval (PMA) applications for diagnostic devices (tests) to the American Food and Drug Administration (FDA). A new test is usually compared to an existing and established test or a general trusted reference. If the existing test (or reference) is not perfect, the FDA recommends to report the positive and negative percent agreement (PPA/NPA). This is calculated using false positives, true positives, false negative and true negatives and calculated like this (1):

  PPA = TP * 100 / (TP + FN)

  NPA = TN * 100 / (TN + FP)

Measures of accuracy

The FDA "recommends you report measures of diagnostic accuracy (sensitivity and specificity pairs, positive and negative likelihood ratio pairs) or measures of agreement (percent positive agreement and percent negative agreement) and their two-sided 95 percent confidence intervals. We recommend reporting these measures both as fractions (e.g., 490/500) and as percentages (e.g., 98.0%)." (2) Sensitivity and specificity are explained here. In general th FDA recommends to report (2):

• the 2x2 table of results comparing the new test with the non-reference standard
• a description of the non-reference standard
• measures of agreement and corresponding confidence intervals.

References:

1. Workshop notes (B. Biswas): Assessing agreement for diagnostic devices  
2. FDA recommendation "Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests" 
3. STAndards for the Reporting of Diagnostic accuracy studies (STARD)
4. Wikipedia page

SNP calling refers to the process of identifying posititions where the genome of a sequenced sample differs to that of the reference genome. This might lead to finding disease-causing genomic alterations.
In the following I wanted to re-align short NGS reads against a specific reference (in this case the Mitochondrial genome sequence). A simple way is to use samtools.

1. make a reference genome index

bwa index -a is NCBI_chrM.fa

2. filter reads

samtools view -F4 –hb A1_S1.bam chrM > A1_S1_chrM.bam
samtools view -f4 -hb A1_S1.bam > A1_S1_unmapped.bam
samtools merge A1_S1_chrM_and_Un.bam A1_S1_chrM.bam A1_S1_unmapped.bam

3. create fastq

bamToFastq -i A1_S1_chrM_and_Un.bam -fq A1_S1_chrM_and_Un.1.fq \
 -fq2 A1_S1_chrM_and_Un.2.fq 2> bwa.err &

4. align to new reference

bwa aln NCBI_chrM.fa A1_S1_chrM_and_Un.1.fq > A1_S1_chrM_and_Un.1.sa
bwa aln NCBI_chrM.fa A1_S1_chrM_and_Un.2.fq > A1_S1_chrM_and_Un.2.sai
bwa sampe NCBI_chrM.fa A1_S1_chrM_and_Un.1.sai A1_S1_chrM_and_Un.2.sai \
 A1_S1_chrM_and_Un.1.fq A1_S1_chrM_and_Un.2.fq > A1_S1_chrM_realigned.sam
samtools view -F4 -Sbh A1_S1_chrM_realigned.sam \
 | samtools sort -o - sorted > A1_S1_chrM_realigned.bam

5. call SNPs

samtools mpileup -uD -f NCBI_chrM.fa A1_S1_chrM_realigned.bam \
 | bcftools view -cg - > A1_S1_chrM_realigned.sam.vcf

From the Samtools help pages:
One should consider to apply the following parameters to mpileup in different scenarios:

• Apply -C50 to reduce the effect of reads with excessive mismatches. This aims to fix overestimated mapping quality and appears to be preferred for BWA-short.
• Given multiple technologies, apply -P to specify which technologies to use for collecting initial INDEL candidates. It is recommended to find INDEL candidates from technologies with low INDEL error rate, such as Illumina. When this option is in use, the value(s) following the option must appear in the PL tag in the @RG header lines.
• Apply -D and -S to keep per-sample read depth and strand bias. This is preferred if there are more than one samples at high coverage.
• Adjust -m and -F to control when to initiate indel realignment (requiring r877+). Samtools only finds INDELs where there are sufficient reads containing the INDEL at the same position. It does this to avoid excessive realignment that is computationally demanding. The default works well for many low-coverage samples but not for, say, 500 exomes. In the latter case, using -m 3 -F 0.0002 (3 supporting reads at minimum 0.02% frequency) is necessary to find singletons.
• Use `-BQ0 -d10000000 -f ref.fa' if the purpose is to get the precise depth of coverage rather than call SNPs. Under this setting, mpileup will count low-quality bases, process all reads (by default the depth is capped at 8000), and skip the time-demanding BAQ calculation.
• Apply -A to use anomalous read pairs in mpileup, which are not used by default (requring r874+).

The VCF format

The Variant Call Format (VCF) is the emerging standard for storing variant data. Originally designed for SNPs and short INDELs, it also works for structural variations.

VCF consists of a header and a data section. The header must contain a line starting with one '#', showing the name of each field, and then the sample names starting at the 10th column. The data section is TAB delimited with each line consisting of at least 8 mandatory fields (the first 8 fields in the table below).

Col	Field	Description
1	CHROM	Chromosome name
2	POS	1-based position. For an indel, this is the position 
		preceding the indel.
3	ID	Variant identifier. Usually the dbSNP rsID.
4	REF	Reference sequence at POS involved in the variant. 
		For a SNP, it is a single base.
5	ALT	Comma delimited list of alternative seuqence(s).
6	QUAL	Phred-scaled probability of all samples being 
		homozygous reference.
7	FILTER	Semicolon delimited list of filters that the variant 
		fails to pass.
8	INFO	Semicolon delimited list of variant information.
9	FORMAT	Colon delimited list of the format of individual 
		genotypes in the following fields.
10+	Sample(s)  Individual genotype information defined by FORMAT.

The following table gives the INFO tags used by samtools and bcftools.

Tag	Description
AC	Allele count in genotypes
AC1	Max-likelihood estimate of the first ALT allele count 
	(no HWE assumption)
AF1	Max-likelihood estimate of the first ALT allele frequency 
	(assuming HWE)
AN	Total number of alleles in called genotypes
CGT	The most probable constrained genotype configuration in the trio
CLR	Log ratio of genotype likelihoods with and without the constraint
DP	Raw read depth (sum for all samples)
DP4	Number of high-quality ref-forward bases, ref-reverse, alt-forward 
	and alt-reverse bases
FQ	Phred probability of all samples being the same
G3	ML estimate of genotype frequencies
HWE	Hardy-Weinberg equilibrium test (PMID:15789306)
ICF	Inbreeding coefficient F
INDEL	Indicates that the variant is an INDEL.
IS	Maximum number of reads supporting an indel and fraction of 
	indel reads
MDV	Maximum number of high-quality nonRef reads in samples
MQ	Root-mean-square mapping quality of covering reads
PC2	Phred probability of the nonRef allele frequency in group1 samples 
	being larger (, smaller) than in group2.
PCHI2	Posterior weighted chi2 P-value for testing the association 
	between group1 and group2 samples.
PR	Number of permutations yielding a smaller PCHI2.
PV4	P-values for strand bias, baseQ bias, mapQ bias and tail 
	distance bias
QBD	Quality by Depth: QUAL/#reads
QCHI2	Phred scaled PCHI2.
RP	# permutations yielding a smaller PCHI2
RPB	Read Position Bias
SF	Source File (index to sourceFiles, f when filtered)
TYPE	Variant type
UGT	The most probable unconstrained genotype configuration in the trio
VDB	Variant Distance Bias (v2) for filtering splice-site artefacts 
	in RNA-seq data.

Sources:

• Angus docu
• Samtools docu
• BaRC wiki

To block a specific IP address from network access to your (Ubuntu Linux) system, you can add it to your firewall settings:
sudo iptables -A INPUT -s 223.4.208.56 -j DROP
To remove this entry:
sudo iptables -D INPUT -s 223.4.208.56 -j DROP
To just list current firewall rules:
sudo iptables -L

Sources: cyberciti.biz

There are many sophisticated services and scripts to monitor the accessability of your website or various aspects of your web server. Check stack-overflow and look at Monastic for examples. Here is a very simple solution I needed to monitor the availability of a specific server using its IP address within the internal network. Is was necessary after the server's IP address, that is used in third party software to provide specific services, was "stolen" by other machines. In this case the DNS server assigned the IP address that should have been reserved to mobile devices that connected to the wireless network.

This approach is simply fetching a website from a specific URL using the "reserved" IP and looks for a word/pattern you know should be there. The script is run on a second machine (host name "ubuntu64"), an Ubuntu VM. (It is not using any additional security measures you will want to use if you expose the machine externally.)

Prepare second machine to send notification emails:
Install sendmail, sendemail, mailutils, sensible-mda (to have the whole set).
Add/modify entry in /etc/hosts:

127.0.1.1 ubuntu64.network.local ubuntu64

run "sudo sendmailconfig"
test with

Code

Write bash script to get and check website and send alert emails:

Code

Add a crontab entry to automatically run this script every 10 minutes:

Code

Additional improvements could include the options to stop alerting after a specific number of alerts or checking the response time.

Alternatively you can just look up the MAC address associated with the "reserved" IP and compare it to the known physical address of your server and wrap this up into a little script:

>arp -a 192.168.1.1

Interface: 192.168.1.152 --- 0xb
  Internet Address      Physical Address      Type
  192.168.1.1           00-11-18-2c-2e-6d     dynamic

The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences. It is a text format for storing sequence data in a series of tab delimited ASCII columns and is commonly used in next-generation sequencing data processing. It is the (non-binary) human-readable version of the BAM format and contains information about the read and the aligned position in the genome. It was developed by Heng Li in Richard Durbins group and others, their paper is here.

After a header section the alignment section describes all results of the aligned read data. The format is best explained with an example line:

Code

Fieldname	description	Example-data
QNAME	read name	1:497:R:-272+13M17D24M
FLAG	alignment flag	113
RNAME	alignment chromosome	1
POS	alignment start position	497
MAPQ	overall mapping quality	37
CIGAR	alignment CIGAR string	37M
MRNM/RNEXT	name of next alignm. in group (mate)	15
MPOS/PNEXT	pos. of next alignm. in group (mate)	100338662
ISIZE/TLEN	observed Template LENgth	0
SEQ	sequence	CGGGTCTGACCTGAGGAGAACTGTGCTCCGCCTTCAG
QUAL	quality per base	0;==-==9;>>>>>=>>>>>>>>>>>=>>>>>>>>>>
TAGs	further tags with alignment info
XT:A:U NM:i:0 SM:i:37 AM:i:0 X0:i:1 X1:i:0 XM:i:0 XO:i:0 XG:i:0 MD:Z:37

The tags are optional and might vary between alignment programs. Shown are examples from BWA. Important for filtering are usually the tags X0:i (numbers of genome alignments of this read) and XM:i (number of mismatches in alignment).

       Tag	Meaning
       NM	Edit distance
       MD	Mismatching positions/bases
       AS	Alignment score
       BC	Barcode sequence
       X0	Number of best hits
       X1	Number of suboptimal hits found by BWA
       XN	Number of ambiguous bases in the referenece
       XM	Number of mismatches in the alignment
       XO	Number of gap opens
       XG	Number of gap extentions
       XT	Type: Unique/Repeat/N/Mate-sw
       XA	Alternative hits; format: (chr,pos,CIGAR,NM;)*
       XS	Suboptimal alignment score
       XF	Support from forward/reverse alignment
       XE	Number of supporting seeds

The read name (at least from Illumina machines) are constructed as:

[instrument-name]:[run ID]:[flowcell ID]:[lane-number]:[tile-number]:
[x-pos]:[y-pos] [read number]:[is filtered]:[control number]:
[barcode sequence]

example:

@M01117:25:000000000-A37B9:1:1101:14984:1386 1:N:0:4

Sources:
genome.sph.umich.ed with further useful details, full specs.

To display the current date, day of the week and time on a web page, you don't want to refresh the entire page every sencond or minute. Instead you will want to use JavaScript to dynamically update just this date/clock display element. Here is the code for a display in the format

Friday, 10.8.2012    9:41:49

Code

Sources: trans4mind.com, w3schools.com

To run programs or pipelines automatically it is often necessary to create or adjust configuration files. Ideally this should be done dynamically by a script from a skeleton (layout) file, replacing placeholder with the adjusted values. This can be done with a unix shell script that even contains the skeleton within:

Code

Save as script.sh and call with parameters:
sh script.sh program_name par1 par2

Source: stackoverflow

Sorting (elements in an array) is a very common tasks in many scripts. A lot of research has gone into finding the most efficient way to sort.
In Ruby the "sort" function performs a standard comparison accoring to the data type inspected, but as in most other languages you can define any specific orders.

   open_orders.sort

is equivalent to

   open_orders.sort { |x, y| x <=> y }

The sort algorithm will assume that this comparison function/block will return a value accoring to the following logic (like the comparison operators):

    return -1 if x < y
    return  0 if x = y
    return  1 if x > y

So using this logic I can define a specific custom function to to compare the elements that need sorting and call it in the sort function afterwards. In my simple example I need to sort order numbers by two criteria: by a string first ("UK" before "ORD") and by ascending numbers afterwards.

Code

Source: stackoverflow.com

In cases where two copies of the same chromosome, or part of a chromosome, from one parent and no copies from the other parent are present in the cell, we call it uniparental disomy (UPD). While all DNA information is present, the development of the cell (and the organism) is hindered because of missing / wrong epigenetic markers. The basic mechanism of how this faulty distribution of chromosomes can occur, is shown in fig.1.

Sources:

• Wikipedia
• Eggermann and Kotzot (2010) Uniparental disomy, Onset mechanisms and their relevance in clinical genetics [German], Medizinische Genetik

Besides the visual client, the version control system Perforce can be operated through the command line (unix prompt or windows Dos window) and therefor be controlled through other programs like MatLab:

[status, result] = dos(p4command);

A reference manual is available, here are a few hints:
Check the environment settings:

p4 set
  P4CHARSET=winansi
  P4CLIENT=try1 (set)
  P4EDITOR=C:\Windows\SysWOW64\notepad.exe (set)
  P4PORT=perforce:1666
  P4USER=Felix_Kokocinski

end edit if necessary with

set P4CHARSET=winansi

P4EDITOR is optional, P4CLIENT is the checkout / workspace name.
The settings can also be set permanently in the visual client under
Edit / Preferences / Connection / Change Settings
If these are wrong you will get messages like "file(s) not on client".

Most common commands:
synchronize repository:

p4 sync

checkout file:

p4 edit filename.txt
  or
p4 edit //depot/path/in/perforce/filename.txt

submit changes:

p4 submit -d "description of changes" filename.txt

revert to version in repository:

p4 revert filename.txt

add new file:

p4 add filename.txt

get help:

p4 help

Here are some useful one-liners for various tasks.

The symbols to describe the different nucleotides in DNA are the following:

------------------------------------------
Symbol       Meaning      Nucleic Acid
------------------------------------------
A            A           Adenine
C            C           Cytosine
G            G           Guanine
T            T           Thymine
U            U           Uracil
M          A or C
R          A or G
W          A or T
S          C or G
Y          C or T
K          G or T
V        A or C or G
H        A or C or T
D        A or G or T
B        C or G or T
X      G or A or T or C
N      G or A or T or C

Note: these letters are also used in the "samtools tview" program to visually show NGS read alignments.

Sources:

• http://www.chick.manchester.ac.uk/SiteSeer/IUPAC_codes.html
• IUPAC-IUB SYMBOLS FOR NUCLEOTIDE NOMENCLATURE: Cornish-Bowden (1985) Nucl. Acids Res. 13: 3021-3030.
• SEQanswers

Telomeres form caps on the ends of chromosomes that prevent fusion of chromosomal ends and provide genomic stability.

During gametogenesis, reprogramming of the germ cells leads to elongation of telomeres up to their species-specific maximum.

In normal somatic cells, telomeres are progressively shortened with every cell division. This shortening in normal human cells limits the number of cell divisions. For human cells to proliferate beyond the senescence checkpoint, they need to stabilize telomere length. This is accomplished mainly by reactivation of the telomerase enzyme. Telomerase expression is under the control of many factors. Expression of telomerase can lead to cell immortalization and is activated during tumorigenesis, i.e. cancer.

Male Xq-telomeres are 1100 bp shorter than female Xq-telomeres.

The telomeric repeat found on all human chromosomes is "TTAGGG".

The centromeres and telomeres of the human chromosomes are not defined as region attributes in the Ensembl perl API explicitely, so for checking these regions, one option is to pull them out of the UCSC table browser (use the "Mapping and Sequencing tracks" group and the "Gap" table) and define them manually. You can e.g. create an array of hashes with the regions and use them in your script:

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The list of centromere regions (transformed from the 0-based UCSC system to the 1-based coordinated system) for GRCh37 is:

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Telomeres of chromosome 17 have not been defined for assembly GRCh37. They are short, but do exists nonetheless. An assembly patch will address this.

Sources:

• Rashid-Kolvear et al. 2007
• Moyzis et al. 1988
• Perner et al. 2003

The karyotype (number and set-up of the chromosomes) of a person or any changes in specific regions on human chromosomes are described with a system of numbers and symbols, defined by a group of cytogentic experts as the International System for Human Cytogenetic Nomenclature (ISCN). It was initiated 1960 by a committee after the suggestion of Charles E. Ford resulting in the "Proposed Standard System of Nomenclature of Human Mitotic Chromosomes". The system is based on the ideogram definitions of visual bands (described eg. in Francke et al. 1981), and was last revised 2005 and 2009.

The visual bands are created by staining techniques and describe regions of similarity in respect to functionality and base compositions (GC content); their lengths are 5-10 MB.

Example ideograms: Human chromosome 1 in different resolutions, from WashU

Numbers used:

The regions are numbered from the centromer outwards in both directions towards the telomeres on the shorter p arm and the longer q arm. The numbers cannot be read in the normal decimal numeric system e.g. 21, but rather 2-1 (region 2 band 1). Counting starts at the centromer as region 1 (or 1-0), to 11 (1-1) to 21 (2-1) to 22 (2-2) etc. Subbands are added in a similar way, eg. 21.1 to 21.2, if the bands are small or only appear at a higher resolution.

There are different levels of resolution that can be used as bands, e.g. 1q32 in the 400-bands resolution can be split up into 1q32.1, 1q32.2, 1q32.3 in the 550-bands resolution (see Figure above as an example for chr 1).

Here is a list of chromosome, band (arm, region, band, subband), genomic start and end position, from the Ensembl database for assembly GRCh37:

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