https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/
一　What is FastQC
Modern high throughput sequencers can generate hundreds of millions of sequences in a single run. Before analysing this sequence to draw biological conclusions you should always perform some simple quality control checks to ensure that the raw data looks good and there are no problems or biases in your data which may affect how you can usefully use it.

Most sequencers will generate a QC report as part of their analysis pipeline, but this is usually only focused on identifying problems which were generated by the sequencer itself. FastQC aims to provide a QC report which can spot problems which originate either in the sequencer or in the starting library material.

FastQC can be run in one of two modes. It can either run as a stand alone interactive application for the immediate analysis of small numbers of FastQ files, or it can be run in a non-interactive mode where it would be suitable for integrating into a larger analysis pipeline for the systematic processing of large numbers of files.
对于我们而言　我们更可能的是使用非图形化的模式　将其集成到我们的流程中

二　使用
Opening a Sequence file
To open one or more Sequence files interactively simply run the program and select File > Open. You can then select the files you want to analyse.

Newly opened files will immediately appear in the set of tabs at the top of the screen. Because of the size of these files it can take a couple of minutes to open them. FastQC operates a queueing system where only one file is opened at a time, and new files will wait until existing files have been processed.

FastQC supports files in the following formats

FastQ (all quality encoding variants)
Casava FastQ files*
Colorspace FastQ
GZip compressed FastQ
SAM
BAM
SAM/BAM Mapped only (normally used for colorspace data)

• Casava fastq format is the same as regular fastq except that the data is usually split across multiple files for a single sample. In this mode the program will merge the files in a sample group and present a single report for each sample. Also Casava fastq files contain poor quality sequences which have been flagged to be remove. In Casava mode the program will exclude these flagged sequences from the report.

By default FastQC will try to guess the file format from the name of the input file. Anything ending in .sam or .bam will be opened as a SAM/BAM file (using all sequences, mapped and unmapped) , and everything else will be treated as FastQ format. If you want to override this detection and specify the file format manually then you can use the drop down file filter in the file chooser to select the type of file you're going to load. You need to use the drop down selector to make the program use the Mapped BAM or Casava file modes as these won't be selected automatically.

Evaluating Results
The analysis in FastQC is performed by a series of analysis modules. The left hand side of the main interactive display or the top of the HTML report show a summary of the modules which were run, and a quick evaluation of whether the results of the module seem entirely normal (green tick), slightly abnormal (orange triangle) or very unusual (red cross).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the modules section of the help.

Saving a Report
In addition to providing an interactive report FastQC also has the option to create an HTML version of this report for a more permanent record. This HTML report can also be generated directly by running FastQC in non-interactive mode.

To create a report simply select File > Save Report from the main menu. By default a report will be created using the name of the fastq file with _fastqc.html appended to the end. The report will be created for whichever file tab was active when the menu option was selected.

The HTML file which is saved is a self-contained document with all of the graphs embedded into it, so you can distribute this single file. Alongside the HTML file is a zip file (with the same name as the HTML file, but with .zip added to the end). This file contains the graphs from the report as separate files but also contains data files which are designed to be easily parsed to allow for a more detailed and automated evauation of the raw data on which the QC report is built.

三　指标详解
Basic Statistics
Summary
The Basic Statistics module generates some simple composition statistics for the file analysed.

Filename: The original filename of the file which was analysed
File type: Says whether the file appeared to contain actual base calls or colorspace data which had to be converted to base calls
Encoding: Says which ASCII encoding of quality values was found in this file.
Total Sequences: A count of the total number of sequences processed. There are two values reported, actual and estimated. At the moment these will always be the same. In the future it may be possible to analyse just a subset of sequences and estimate the total number, to speed up the analysis, but since we have found that problematic sequences are not evenly distributed through a file we have disabled this for now.
Filtered Sequences: If running in Casava mode sequences flagged to be filtered will be removed from all analyses. The number of such sequences removed will be reported here. The total sequences count above will not include these filtered sequences and will the number of sequences actually used for the rest of the analysis.
Sequence Length: Provides the length of the shortest and longest sequence in the set. If all sequences are the same length only one value is reported.
%GC: The overall %GC of all bases in all sequences

Per Base Sequence Quality
Summary
This view shows an overview of the range of quality values across all bases at each position in the FastQ file.

For each position a BoxWhisker type plot is drawn. The elements of the plot are as follows:

The central red line is the median value
The yellow box represents the inter-quartile range (25-75%)
The upper and lower whiskers represent the 10% and 90% points
The blue line represents the mean quality
The y-axis on the graph shows the quality scores. The higher the score the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

It should be mentioned that there are number of different ways to encode a quality score in a FastQ file. FastQC attempts to automatically determine which encoding method was used, but in some very limited datasets it is possible that it will guess this incorrectly (ironically only when your data is universally very good!). The title of the graph will describe the encoding FastQC thinks your file used.

Results from this module will not be displayed if your input is a BAM/SAM file in which quality scores have not been recorded.

Per Sequence Quality Scores
Summary
The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, often because they are poorly imaged (on the edge of the field of view etc), however these should represent only a small percentage of the total sequences.

If a significant proportion of the sequences in a run have overall low quality then this could indicate some kind of systematic problem - possibly with just part of the run (for example one end of a flowcell).

Results from this module will not be displayed if your input is a BAM/SAM file in which quality scores have not been recorded.

Per Base Sequence Content
Summary
Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis. It will however produce a warning or error in this module.

Per Sequence GC Content
Summary
This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Per Base N Content
Summary
If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base] call

This module plots out the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Sequence Length Distribution
Summary
Some high throughput sequencers generate sequence fragments of uniform length, but others can contain reads of wildly varying lengths. Even within uniform length libraries some pipelines will trim sequences to remove poor quality base calls from the end.

This module generates a graph showing the distribution of fragment sizes in the file which was analysed.

In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment.

Duplicate Sequences
Summary
In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification).

This module counts the degree of duplication for every sequence in a library and creates a plot showing the relative number of sequences with different degrees of duplication.

To cut down on the memory requirements for this module only sequences which first appear in the first 100,000 sequences in each file are analysed, but this should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level. To cut down on the amount of information in the final plot any sequences with more than 10 duplicates are placed into grouped bins to give a clear impression of the overall duplication level without having to show each individual duplication value.

Because the duplication detection requires an exact sequence match over the whole length of the sequence, any reads over 75bp in length are truncated to 50bp for the purposes of this analysis. Even so, longer reads are more likely to contain sequencing errors which will artificially increase the observed diversity and will tend to underrepresent highly duplicated sequences.

The plot shows the proportion of the library which is made up of sequences in each of the different duplication level bins. There are two lines on the plot. The blue line takes the full sequence set and shows how its duplication levels are distributed. In the red plot the sequences are de-duplicated and the proportions shown are the proportions of the deduplicated set which come from different duplication levels in the original data.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot. These high duplication peaks will most often appear in the blue trace as they make up a high proportion of the original library, but usually disappear in the red trace as they make up an insignificant proportion of the deduplicated set. If peaks persist in the blue trace then this suggests that there are a large number of different highly duplicated sequences which might indicate either a contaminant set or a very severe technical duplication.

The module also calculates an expected overall loss of sequence were the library to be deduplicated. This headline figure is shown at the top of the plot and gives a reasonable impression of the potential overall level of loss.

Overrepresented Sequences
Summary
A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

This module lists all of the sequence which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

For each overrepresented sequence the program will look for matches in a database of common contaminants and will report the best hit it finds. Hits must be at least 20bp in length and have no more than 1 mismatch. Finding a hit doesn't necessarily mean that this is the source of the contamination, but may point you in the right direction. It's also worth pointing out that many adapter sequences are very similar to each other so you may get a hit reported which isn't technically correct, but which has very similar sequence to the actual match.

Because the duplication detection requires an exact sequence match over the whole length of the sequence any reads over 75bp in length are truncated to 50bp for the purposes of this analysis. Even so, longer reads are more likely to contain sequencing errors which will artificially increase the observed diversity and will tend to underrepresent highly duplicated sequences.

Adapter Content
Summary
The Kmer Content module will do a generic analysis of all of the Kmers in your library to find those which do not have even coverage through the length of your reads. This can find a number of different sources of bias in the library which can include the presence of read-through adapter sequences building up on the end of your sequences.

You can however find that the presence of any overrepresented sequences in your library (such as adapter dimers) will cause the Kmer plot to be dominated by the Kmers these sequences contain, and that it's not always easy to see if there are other biases present in which you might be interested.

One obvious class of sequences which you might want to analyse are adapter sequences. It is useful to know if your library contains a significant amount of adapter in order to be able to assess whether you need to adapter trim or not. Although the Kmer analysis can theoretically spot this kind of contamination it isn't always clear. This module therefore does a specific search for a set of separately defined Kmers and will give you a view of the total proportion of your library which contain these Kmers. A results trace will always be generated for all of the sequences present in the adapter config file so you can see the adapter content of your library, even if it's low.

The plot itself shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Kmer Content
Summary
The analysis of overrepresented sequences will spot an increase in any exactly duplicated sequences, but there are a different subset of problems where it will not work.

If you have very long sequences with poor sequence quality then random sequencing errors will dramatically reduce the counts for exactly duplicated sequences.
If you have a partial sequence which is appearing at a variety of places within your sequence then this won't be seen either by the per base content plot or the duplicate sequence analysis.
The Kmer module starts from the assumption that any small fragment of sequence should not have a positional bias in its apearance within a diverse library. There may be biological reasons why certain Kmers are enriched or depleted overall, but these biases should affect all positions within a sequence equally. This module therefore measures the number of each 7-mer at each position in your library and then uses a binomial test to look for significant deviations from an even coverage at all positions. Any Kmers with positionally biased enrichment are reported. The top 6 most biased Kmer are additionally plotted to show their distribution.

To allow this module to run in a reasonable time only 2% of the whole library is analysed and the results are extrapolated to the rest of the library. Sequences longer than 500bp are truncated to 500bp for this analysis.

Per Tile Sequence Quality
Summary
This graph will only appear in your analysis results if you're using an Illumina library which retains its original sequence identifiers. Encoded in these is the flowcell tile from which each read came. The graph allows you to look at the quality scores from each tile across all of your bases to see if there was a loss in quality associated with only one part of the flowcell.

The plot shows the deviation from the average quality for each tile. The colours are on a cold to hot scale, with cold colours being positions where the quality was at or above the average for that base in the run, and hotter colours indicate that a tile had worse qualities than other tiles for that base. In the example below you can see that certain tiles show consistently poor quality. A good plot should be blue all over.

Reasons for seeing warnings or errors on this plot could be transient problems such as bubbles going through the flowcell, or they could be more permanent problems such as smudges on the flowcell or debris inside the flowcell lane.

四　使用模式
图形化的交互式模式和命令行模式
fastqc somefile.txt someotherfile.txt
不带参数的话就会默认打开图形界面。
对于命令行模式　会生成一些报表　这些报表怎么自动化的分析呢？
咨询诺禾的结果是：
１。对于能自动判断的QC指标　就程序自己做
２。不能自动判定的　就人工做　人工这部分会暂停流程

测试过程如果报内存不足　
需要修改fastqc 文件 增加内存量
push @java_args,"-Xmx64G";
if ($java_bin ne 'java') {
system $java_bin,@java_args, "uk.ac.babraham.FastQC.FastQCApplication", @files;
}
else {
exec $java_bin,@java_args, "uk.ac.babraham.FastQC.FastQCApplication", @files;
}