gffread my.gff3 -T -o my.gtf

See gffread -h for more information

The Mauve Contig Mover (MCM) can be used to order a draft genome relative to a related reference genome.  The functionality of this software module has been described in Rissman et al. 2009 , a publication in Bioinformatics. The Mauve Contig Mover can ease a comparative study between draft and reference sequences by ordering draft contigs according to the reference genome.  In many cases, true rearrangements in the draft relative to the reference can be identified.  The quality of the reorder is limited by the distance between the sequences, as indicated by the amount of shared gene content among the two organisms.  A more distant reference will usually yield fewer ordered draft genome contigs, and may also induce erroneous placements of draft contigs.  In addition to ordering contigs, MCM also orient them in the most likely orientation, and, if annotated sequence features are specified in an input file (e.g. with GenBank format input for the draft), MCM will output adjusted coordinates ranges for the features.

Using Mauve Contig Mover

Mauve Contig Mover can be launched from the Tools->Order Contigs Menu of The Mauve Viewer.

Once the Mauve Contig Mover has been launched, it starts by requesting the user to specify an output directory.  MCM will create a series of mauve alignments in the output directory, structured into several subfolders, so creating a new, empty output folder is often best to minimize clutter.  The output directory selection is shown below.

Once the output directory has been set, a window similar to the Progressive Mauve Alignment Window appears.  Using the Progressive Mauve alignment window is described in more detail in the section “Constructing a genome alignment “, but when used for reordering contigs,  the following additional notes and constraints apply:
1.    Only two sequences should be entered.  The first must always be the reference, the second the draft genome to reorder.  The first may also be a draft, but only the second will be reordered.
2.    The reference genome may be in any of the allowable file formats, the draft must either be in a fasta or genbank file.  If the draft is in genbank format, a unique identifier must be specified in either the LOCUS tag of each contig.
3.    The alignment parameters have already been adjusted for what is generally best for draft genomes.  This may depend on the draft and reference, and can be adjusted if needed.

The Reordering Process

The reordering will begin when the start button is pressed.  It is an iterative process, and may take anywhere from a half hour to several hours.  It may be cancelled at any point (intermediate results will be viewable).  If it is canceled after the first reorder the reordered draft genome will be available in a Multi-FastA file in the corresponding output directory, although an alignment of the canceled ordering step will not be present.  If the ordering process is not manually ended, it will terminate when it finds an order has repeated.  Sometimes the order will cycle through several possibilities; this indicates it cannot determine which of them is most likely. Alignment parameters may be changed before reorder starts or any time between alignments.
The following message will appear when the reorder process is complete:

The MCM Output Files

MCM will output a series of folders called alignment1-alignmentX, representing each iteration of the reorder.  Each has the standard Mauve alignment files, as described in the section Mauve Output File Formats .  Each folder also has an additional file called name_of_genome_contigs.tab, where “name_of_genome” is the draft genome’s name.  This file is included for ease of interpreting reorder results, and also acts as an index to the fasta as the contig orders and orientations change (even if the draft was originally input as a genbank, after the first alignment, it will be converted to a fasta with annotation information preserved in a file described below).  The file is divided into 3 sections, each containing a list of contigs.  The data for each contig includes its label (name), its location in the genome (numbered in pseudocoordinates from the first to last contig; these coordinates can be entered into the View->Go To->Sequence Position menu option to jump to that contig using the Mauve Alignment Viewer), and whether it is oriented the same as originally input, or was complemented.  The three sections are described below:

1.    Contigs to reverse:  This section contains contigs whose order is reversed with respect to the previous iteration.  Note that contigs in this section may be oriented the same as originally input, this can be determined from the forward orcomplement designation.

2.    Ordered Contings:    This is a list of all the contigs in the order and orientation they appear in the fasta for the draft of this iteration of the reorder.  Since these include all the contigs in the original input, those with no ordering information (no aligned region) will be clustered at the end.  These will appear as contigs with no LCBs at the end of the draft genome.

3.    Contigs with Conflicting Order information:    This is a list of contigs containing LCBs suggesting multiple possible locations.  These may be of interest to verify positioning, or to look at points of potential rearrangement or misassembly.

If the draft was input as an annotated genbank file, a second file will appear in each alignment folder called name_of_genome_features.tab.  This file will contain a line for each annotation, information about its current orientation and location (which will change if the contig is inverted), coordinates from the previous iteration (indicating relative orientation), and whether it is reversed from the original input.  It will also have a label field used to identify each feature.  This will be gotten from the annotation, as checked in the following order:  db_xref, label, gene, and locus_tag.

Thus, the folder with the highest numbered alignment contains a fasta and one (or possibly two) descriptor files representing the final order of the draft genome.

Reordering contigs from the command-line (batch mode)

In situations where it is necessary to order contigs in a large number of draft genomes it is often more desirable to automate the process using command-line interfaces and scripts. Mauve Contig Mover supports command-line operation through the Mauve Java JAR file.

Given a reference genome file called “reference.gbk” and a draft genome called “draft.fasta”, one would invoke the reorder program with the following syntax:

java -Xmx500m -cp Mauve.jar org.gel.mauve.contigs.ContigOrderer -output results_dir -ref reference.gbk -draft draft.fasta

The file Mauve.jar is part of the Mauve distribution.  On windows systems it can usually be found in C:\Program Files\Mauve X\Mauve.jar where X is the version of Mauve.  On Mac OS X it is located inside the Mauve application.  For example, if Mauve has been placed in the OS X applications folder, Mauve.jar can be found at /Applications/Mauve.app/Contents/Resources/Java/Mauve.jar.  On Linux, Mauve.jar is simply at the top level of the tar.gz archive.  In the above example command, it will be necessary to specify the full path to the Mauve.jar file.

http://asap.ahabs.wisc.edu/mauve-aligner/mauve-user-guide/reording-contigs-in-draft-genomes.html

Results: To address these challenges, we realign reads against the reference using our previously proposed hidden Markov model (HMM) that models homopolymer errors and then merges these pairwise alignments into a weighted alignment graph. Based on our weighted alignment graph and HMM, we develop a method called PyroHMMvar which can simultaneously detect short indels and SNPs, as demonstrated in human resequencing data. Specifically, by applying our methods to simulated diploid datasets, we demonstrate that PyroHMMvar produces more accurate results than state-of-the-art methods, such as Samtools and GATK, and is less sensitive to mapping parameter settings than the other methods. We also apply PyroHMMvar to analyze one human whole genome resequencing dataset and the results confirm that PyroHMMvar predicts SNPs and indels accurately.

Availability and Implementation: Source code freely available at the following URL: https://code.google.com/p/pyrohmmvar/, implemented in C++ and supported on Linux.

                                              微生物基因组中的GC-skew
在大多数细菌基因组中，我们注意到前导链(leading strand)和滞后链(lagging strand)在碱基组成上存在很明显的不同——前导链富含G和T，而滞后链中的A和C更多一些。打破A=T和C=G的碱基频率发生的偏移，被称之为“AT偏移(AT-skew)”和“GC偏移(GC-skew)”。由于通常GC偏移比AT偏移发生的更明显，所以我们更多地只考虑GC偏移。衡量GC偏移的一个方法是延基因序列做一个滑动窗口(sliding window)，计算(G-C)/(G+C)的值并绘图。这个公式给出了G超过C的百分比含量——值为正，则代表的是前导链；值为负，则为滞后链。

（图片来源：Nature.com）
是什么引起了GC偏移呢？我们对此还知之甚少。可能是因为前导链和滞后链在以单链DNA(single-stranded DNA)形态进行复制的时候两者花费的时间不同，所以易受不同的突变压力影响，从而导致暴露在不同的DNA受损环境之中。由于T-G和G-T的碱基互补配对错位(mispair)多于C-A和A-C，所以更容易出错的链(error-prone strand)可能相对地富含G和T.另一个理论依托于胞嘧啶脱氨水解(hydrolytic deamination of cytosine)，这一过程显著地发生在单链DNA之中。复制叉(Replication fork)的非对称结构使得滞后链模板产生暂时性单链，使之更容易发生胞嘧啶脱氨。胞嘧啶脱氨导致生成尿嘧啶，其在复制过程中和鸟嘌呤互补配对，实质是引起了C到T的突变。因此，C到T的脱氨基作用将增加那条链中G和T的百分比含量和其互补链中的C和A的百分比含量。
为什么分析GC偏移很重要呢？因为GC偏移在前导链中是正值而在滞后链中为负值，所以GC偏移值是前导链起点、终点以及转变成滞后链的信号，反之亦然。这使得GC偏移成为在环状染色体(circular chromosomes)中标记起点和终点的一个有用的工具。曲线图中显而易见的局部的变化，可以标记出例如近来反向序列的重组或者与外源DNA的同化。DNA的丢失不会造成GC偏移曲线基本形状的改变，尽管和外部DNA新近的合成可能将会对局部方差产生影响。
实际上，GC偏移的可视化会遭受局部波动的影响。所以最好利用GC偏移的累积量，其值是计算序列中任意某一起点到指定点中相邻滑动窗口GC偏移值的总和。图中所示为Wolinella succinogenes DSM1740基因组的GC偏移值和GC偏移累加值，并表明了GC偏移值如何改变了复制起点和终点的信号。GC偏移累加值分别在这些位置上标记出了最大值和最小值。

文章来源：http://www.nature.com/nrmicro/journal/v2/n11/box/nrmicro1024_BX1.html

第二代测序技术中Solexa以及它的升级版Hiseq，目前使用最多。为了帮助PLoB网友进一步了解Solexa相关的概念。与大家分享一篇网上看到的文章《Solexa测序技术中常见术语解释》，文章后面有参考来源链接。更多相关信息欢迎加入PLoB 2000人的生物信息QQ群（群号：235461986）来讨论，有相关测序以及生物信息学问题需要解答欢迎前来。下面直接附上相关的解释。大家同时可以结合上面的示意图，了解Solexa与Hiseq的基本结构。

SBS：边合成边测序反应，每次SBS会延伸一个碱基，大约耗时70分钟。

Run：单次上机测序反应，可以产生4G-75G测序通量不等。

Lane：单泳道，每条泳道可以直接物理区分测序样品，1次run最多可以同时上样8条Lane。

Channel：Lane的同义词。

Tile：小区，每条Lane中排有2列tile，合计120个小区。每个小区上分布数目繁多的簇结合位点。

Cluster：簇，在Solexa测序技术中会采用桥式PCR方式生产DNA簇，每个DNA簇才能产生亮度达到CCD可以分辨的荧光点。

Index：标签，在Solexa多重测序（Multiplexed Sequencing）过程中会使用Index来区分样品，并在常规测序完成后，针对Index部分额外进行7个循环的测序，通过Index的识别，可以在1条Lane中区分12种不同的样品。

Barcode: Index同义词

Fasta：一种序列存储格式。一个序列文件若以FASTA格式存储，则每一条序列的第一行以“>”开 头，而跟随“>”的是序列的ID号（即唯一的标识符）及对该序列的描述信息；第二行开始是序列内容，序列短于61nt的，则一行排列完；序列长于 61nt的，则每行存储61nt，最后剩下小于61nt的，在最后一行排列完；第二条序列另起一行，仍然由“>”和序列的ID号开始，以此类推。

Fastq：Fastq是Solexa测序技术中一种反映测序序列的碱基质量的文件格式。第一行以“@”符号开头，后面紧跟一个序列的描述信息；第二行是该序列的内容；第三行以“+”符号开头，后面紧跟的内容与第一行一样，同样是该序列的描述信息；而第四行是第二行中的序列内容每个碱基所对应的测序质量值。

PF%：PF%是指符合测序质量标准的簇的百分比（Multiplexed Sequencing），与测序的通量相关联。

Read：Solexa是成簇反应的，每个簇对应一条DNA序列片段，成为一个read。

名词解释与图片的参考来源：http://www.igenomics.com.cn:7001/ajgene/jsp/ajweb/News.jsp?cid=C47825F27EC00001B8BF8B8D11C01D10

Results: RazerS is a read mapping program with adjustable sensitivity based on counting q-grams. In this work we propose the successor RazerS 3 which now supports shared-memory parallelism, an additional seed-based filter with adjustable sensitivity, a much faster, banded version of the Myers’ bit-vector algorithm for verification, memory saving measures and support for the SAM output format. This leads to a much improved performance for mapping reads, in particular long reads with many errors. We extensively compare RazerS 3 with other popular read mappers and show that its results are often superior to them in terms of sensitivity while exhibiting practical and often competetive run times. In addition, RazerS 3 works without a precomputed index.

Availability and Implementation: Source code and binaries are freely available for download at http://www.seqan.de/projects/razers. RazerS 3 is implemented in C++ and OpenMP under a GPL license using the SeqAn library and supports Linux, Mac OS X, and Windows.

Contact: david.weese@fu-berlin.de

Results: We have developed Qualimap, a Java application that supports user-friendly quality control of mapping data, by considering sequence features and their genomic properties. Qualimap takes sequence alignment data and provides graphical and statistical analyses for the evaluation of data. Such quality-control data are vital for highlighting problems in the sequencing and/or mapping processes, which must be addressed prior to further analyses.

Availability: Qualimap is freely available fromhttp://www.qualimap.org