fastqc结果解析

Analysis module

Opening a Sequence file

To open one or more Sequence files interactively交互式simply run the program and select File > Open. You can then select the files you want to analysis.

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运行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)

?CasavaFastQ files*

?ColorspaceFastQ

?GZip compressed FastQ

?SAM

?BAM

?SAM/BAM Mapped only (normally used for colorspace data)

* Casavafastq 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 groups and present a single report for each sample. Also Casavafastq 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交互显示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强调that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context上下文of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse多样化. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers指示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.

Basic Statistics

Summary

The Basic Statistics module generates some simple composition构成statistics for the file analysed.

?Filename: The original filename of the file which was analysed

?File type: Says whether the file appeared似乎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 问题sequences are not evenly均匀的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

Warning

Basic Statistics never raises a warning.

Failure

Basic Statistics never raises an error.

Common reasons for warnings

This module never raises warnings or errors

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基本原理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分数越高越好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运行后期质量较差, 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错误地(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记录.

Warning

A warning will be issued发布if the lower quartile for any base is less than 10, or if the median for any base is less than 25.

Failure

This module will raise a failure if the lower quartile for any base is less than 5 or if the median for any base is less than 20.

Common reasons for warnings

The most common reason for warnings and failures in this module is a general degradation退化of quality over the duration of long runs. In general sequencing chemistry 化学degrades with increasing read length and for long runs you may find that the general quality of the run falls to a level where a warning or error is triggered.

If the quality of the library falls to a low level then the most common remedy补救is to perform quality trimming整理where reads are truncated截短based on their average quality. For most libraries where this type of degradation has occurred you will often be simultaneously同时running into the issue of adapter read-through so a combined adapter and quality trimming step is often employed.

Another possibility is that a warn / error is triggered because of a short loss of quality earlier in the run, which then recovers to produce later good quality sequence. This can happen if there is a transient瞬间问题problem with the run (bubbles passing through a flowcell for example). You can normally see this type of error by looking at the per-tile quality plot (if available for your platform). In these cases trimming is not advisable明智的as it will remove later good sequence, but you might want to consider masking bases during subsequent mapping or assembly.

If your library has reads of varying不同的length then you can find a warning or error is triggered from this module because of very low coverage for a given base range. Before committing交付to any action, check how many sequences were responsible for triggering an error by looking at the sequence length distribution module results.

Per Sequence Quality Scores

Summary

The per sequence quality score report allows you to see if a subset of your sequences have universally普遍的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比例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.

Warning

A warning is raised if the most frequently observed mean quality is below 27 - this equates to a

0.2% error rate.

Failure

An error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate.

Common reasons for warnings

This module is generally fairly robust相当稳健and errors here usually indicate a general普遍

的loss of quality within a run. For long runs this may be alleviated减轻through quality trimming. If a bi-modal, or complex distribution分布is seen then the results should be evaluated in concert 呼应with the per-tile qualities (if available) since this might indicate the reason for the loss in quality of a subset of sequences.

Per Base Sequence Content

Summary

Per Base Sequence Content plots out the proportion比例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平行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 随机

六聚体(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绝对的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 不利地

affect the downstream analysis. It will however produce a warning or error in this module.

Warning

This module issues a warning if the difference between A and T, or G and C is greater than 10% in any position.

Failure

This module will fail if the difference between A and T, or G and C is greater than 20% in any position.

Common reasons for warnings

There are a number of common scenarios情节which would ellicit a warning or error from this module.

1. Overrepresented sequences: If there is any evidence of overrepresented sequences

such as adapter dimers or rRNA in a sample then these sequences may bias the

overall composition and their sequence will emerge from this plot.

2. Biased fragmentation: Any library which is generated based on the ligation of random

hexamers or through tagmentation should theoretically have good diversity through

the sequence, but experience has shown that these libraries always have a selection

选择性偏差bias in around the first 12bp of each run. This is due to a biased selection

of random primers, but doesn't represent any individually biased sequences. Nearly all

RNA-Seq libraries will fail this module because of this bias, but this is not a problem

which can be fixed by processing, and it doesn't seem to adversely affect the ability to

measure expression.

3. Biased composition libraries: Some libraries are inherently 内在的biased in their

sequence composition. The most obvious example would be a library which has been

treated with sodium bisulphite which will then have converted most of the cytosines to

thymines, meaning that the base composition will be almost devoid of cytosines and

will thus trigger an error, despite this being entirely normal for that type of library

4. If you are analysing a library which has been aggressivley adapter trimmed then you

will naturally introduce a composition bias at the end of the reads as sequences which

happen to match short stretches of adapter are removed, leaving only sequences

which do not match. Sudden deviations 偏差in composition at the end of libraries

which have undergone aggressive 侵略性的trimming are therefore likely to be

spurious.伪造的

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 标准模型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相符合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观测数据data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated受污染的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.

Warning

A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads.

This module will indicate a failure if the sum of the deviations from the normal distribution represents more than 30% of the reads.

Common reasons for warnings

Warnings in this module usually indicate a problem with the library. Sharp peaks on an otherwise smooth distribution are normally the result of a specific contaminant (adapter dimers for example), which may well be picked up by the overrepresented过多被测sequences module. Broader宽阔的peaks may represent contamination with a different species.

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解释the data well enough to make valid有效的base calls.

This module raises a warning if any position shows an N content of >5%.

Failure

This module will raise an error if any position shows an N content of >20%.

Common reasons for warnings

The most common reason for the inclusion内含物of significant proportions of Ns is a general loss of quality, so the results of this module should be evaluated in concert with those of the various quality modules. You should check the coverage of a specific bin, since it's possible that the last bin in this analysis could contain very few sequences, and an error could be prematurely triggered in this case.

Another common scenario方案is the incidence发生率of a high proportions of N at a small number of positions early in the library, against a background of generally good quality. Such deviations差异can occur when you have very biased sequence composition in the library to the point that base callers can become confused and make poor calls. This type of problem will be apparent when looking at the per-base sequence content results.

Sequence Length Distribution

Summary

Some high throughput sequencers generate sequence fragments of uniform 统一的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 可变长度length FastQ files this will show the relative amounts of each different size of sequence fragment.

Warning

This module will raise a warning if all sequences are not the same length.

Failure

This module will raise an error if any of the sequences have zero length.

Common reasons for warnings

For some sequencing platforms it is entirely normal to have different read lengths so warnings here can be ignored.

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富集偏差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效果for the duplication levels in the whole file. Each sequence is tracked跟踪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变平the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants污染物will tend to produce spikes towards the right of the plot. These high duplication peaks will most often appear in the red trace痕迹as they make up a high

proportion of the original library, but usually disappear in the blue 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剧烈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.

Warning

This module will issue a warning if non-unique sequences make up more than 20% of the total. Failure

This module will issue a error if non-unique sequences make up more than 50% of the total. Common reasons for warnings

The underlying潜在的assumption假设of this module is of a diverse unenriched library. Any deviation from this assumption will naturally generate duplicates and can lead to warnings or errors from this module.

In general there are two potential types of duplicate in a library, technical duplicates arising from PCR artefacts, or biological duplicates which are natural collisions where different copies of exactly the same sequence are randomly selected. From a sequence level there is no way to distinguish between these two types and both will be reported as duplicates here.

A warning or error in this module is simply a statement that you have exhausted the diversity in at least part of your library and are re-sequencing the same sequences. In a supposedly 可能的diverse library this would suggest that the diversity has been partially or completely exhausted and that you are therefore wasting sequencing capacity. However in some library types you will naturally tend to over-sequence parts of the library and therefore generate duplication and will therefore expect to see warnings or error from this module.

In RNA-Seq libraries sequences from different transcripts will be present at wildly different levels in the starting population. In order to be able to observe lowly expressed transcripts it is therefore common to greatly over-sequence high expressed transcripts, and this will potentially create large set of duplicates. This will result in high overall duplication in this test, and will often produce peaks in the higher duplication bins. This duplication will come from physically connected regions, and an examination of the distribution of duplicates in a specific genomic region will allow the distinction between over-sequencing and general technical duplication, but these distinctions are not possible from raw fastq files. A similar situation can

arise in highly enriched ChIP-Seq libraries although the duplication there is less pronounced. Finally, if you have a library where the sequence start points are constrained (a library constructed around restriction sites for example, or an unfragmented small RNA library) then the constrained start sites will generate huge dupliction levels which should not be treated as a problem, nor removed by deduplication. In these types of library you should consider using a system such as random barcoding to allow the distinction of technical and biological duplicates.

在RNA测序中很难区分是哪一种重复，最好使用条形码区别。

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 保存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未必一定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.

Warning

This module will issue a warning if any sequence is found to represent more than 0.1% of the total.

This module will issue an error if any sequence is found to represent more than 1% of the total. Common reasons for warnings

This module will often be triggered when used to analyse small RNA libraries where sequences are not subjected to random fragmentation, and the same sequence may natrually be present in a significant proportion of the library.

Adapter Content

Summary

The Kmer Content module will do a generic一般的类的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 通读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.

Warning

This module will issue a warning if any sequence is present in more than 5% of all reads.

This module will issue a warning if any sequence is present in more than 10% of all reads. Common reasons for warnings

Any library where a reasonable proportion of the insert sizes are shorter than the read length will trigger this module. This doesn't indicate a problem as such - just that the sequences will need to be adapter trimmed before proceeding with any downstream analysis.

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 局部的sequence which is appearing at a variety of各种各样的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 appearance 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二项试验test to look for significant deviations偏差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.

Warning

This module will issue a warning if any k-mer is imbalanced with a binomial p-value <0.01.

Failure

This module will issue a warning if any k-mer is imbalanced with a binomial p-value < 10^-5.

Common reasons for warnings

Any individually overrepresented sequences, even if not present at a high enough threshold to trigger the overrepresented sequences module will cause the Kmers from those sequences to be highly enriched in this module. These will normally appear as sharp spikes of enrichemnt at a single point in the sequence, rather than a progressive or broad enrichment.

Libraries which derive from random priming will nearly always show Kmer bias at the start of the library due to an incomplete sampling of the possible random primers.

Per Tile Sequence Quality

Summary

This graph will only appear in your analysis results if you're using an Illumina library which retains保持its original sequence identifiers标识符. 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测量, with cold colours being positions where the quality was at or below 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 瞬变问题such as bubbles going through the flowcell, or they could be more permanent problems such as smudges污点on the flowcell or debris inside the flowcell lane.

Warning

This module will issue a warning if any tile shows a mean Phred score more than 2 less than the mean for that base across all tiles.

Failure

This module will issue a warning if any tile shows a mean Phred score more than 5 less than the mean for that base across all tiles.

Common reasons for warnings

Whilst 同时warnings in this module can be triggered by individual specific events we have also observed that greater variation in the phred scores attributed to tiles can also appear when a flowcell is generally overloaded. In this case events appear all over the flowcell rather than being confined to a specific area or range of cycles. We would generally ignore errors which mildly温和地affected a small number of tiles for only 1 or 2 cycles, but would pursue larger effects which showed high deviation in scores, or which persisted for several cycles.

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《现代机械设计方法》课程结业论文 （ 2011 级） 题目：ABAQUS实例分析 学生姓名 XXXX 学号 XXXXX 专业机械工程 学院名称机电工程与自动化学院 指导老师 XX 2013年 5 月8 日

目录 第一章Abaqus简介 (1) 一、Abaqus总体介绍 (1) 二、Abaqus基本使用方法 (2) 1.2.1 Abaqus分析步骤 (2) 1.2.2 Abaqus/CAE界面 (3) 1.2.3 Abaqus/CAE的功能模块 (3) 第二章基于Abaqus的通孔端盖分析实例 (4) 一、工作任务的明确 (6) 二、具体步骤 (6) 2.2.1 启动Abaqus/CAE (4) 2.2.2 导入零件 (5) 2.2.3 创建材料和截面属性 (6) 2.2.4 定义装配件 (7) 2.2.5 定义接触和绑定约束（tie） (10) 2.2.6 定义分析步 (14) 2.2.7 划分网格 (15) 2.2.8 施加载荷 (19) 2.2.9 定义边界条件 (20) 2.2.10 提交分析作业 (21) 2.2.11 后处理 (22) 第三章课程学习心得与作业体会 (23)

第一章： Abaqus简介 一、Abaqus总体介绍 Abaqus是功能强大的有限元分析软件，可以分析复杂的固体力学和结构力学系统，模拟非常庞大的模型，处理高度非线性问题。Abaqus不但可以做单一零件的力学和多物理场的分析，同时还可以完成系统级的分析和研究。 Abaqus使用起来十分简便，可以很容易的为复杂问题建立模型。Abaqus具备十分丰富的单元库，可以模拟任意几何形状，其丰富的材料模型库可以模拟大多数典型工程材料的性能，包括金属、橡胶、聚合物、复合材料、钢筋混泥土、可压缩的弹性泡沫以及地质材料（例如土壤、岩石）等。 Abaqus主要具有以下分析功能： 1.静态应力/位移分析 2.动态分析 3.非线性动态应力/位移分析 4.粘弹性/粘塑性响应分析 5.热传导分析 6.退火成形过程分析 7.质量扩散分析 8.准静态分析 9.耦合分析 10.海洋工程结构分析 11.瞬态温度/位移耦合分析 12.疲劳分析 13.水下冲击分析 14.设计灵敏度分析 二、Abaqus基本使用方法 1.2.1 Abaqus分析步骤 有限元分析包括以下三个步骤： 1.前处理（Abaqus/CAE）:在前期处理阶段需要定义物理问题的模型，并生 成一个Abaqus输入文件。提交给Abaqus/Standard或 Abaqus/Explicit。 2.分析计算（Abaqus/Standard或Abaqus/Explicit）:在分析计算阶段， 使用Abaqus/Standard或Abaqus/Explicit求解输入文件中所定义的