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Stockholm format to dot-brackets format?

Stockholm format to dot-brackets format?


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I need to convert all my sequences in a Stockholm format into this:

hg19_11_6_Ala ----------------.GG--gggaguggugu… gguuacgaaugUGGCCUCUGC-----AA… GCAGACA… G… CCUGGGUUCAAUU… #=GR hg19_11_6_Ala PP… 22… 45677788888… 899999999996777899988… 56… 8999999… 9… *************…

Into something like this:

hg19_11_6_Ala… ((… (((((((((((… )))))))))))))))))))))))… ((… )))))))))… ))))…

Obviously, being coherent with the stockholm format. Any hint?


If what you want is to find a consensus structure for a group of alignments in stockholm format then you might try with RNAalifold and for single sequence folding check RNAfold. Both have online servers and can also be run offline.

After you get the consensus structure update the stockholm file by adding a structure consensus line as:#=GC SS_consfollowed by the dot bracket notation.

A nice RNA structure editor that will make your life easier is EMACS when complemented with RALEE. It allows you to view and manipulate RNA structures, predict structure folding and color the alignments based on basepairs relationships. It will be worth it to invest time into mastering RALEE.


Contents

Sequences can be read and written in a variety of formats. These can be very confusing for users, but EMBOSS aims to make life easier by automatically recognising the sequence format on input.

That means that if you are converting from using another sequencing package to EMBOSS and you have your existing sequences in a format that is specific for that package, for example GCG format, you will have no problem reading them in.

If you don't hold your sequence in a recognised standard format, you will not be able to analyse your sequence easily.

When we talk about 'sequence format' we are NOT talking about any sort of program-specific format like a word processor format or text formatting language , so we are not talking about things like: 'NOTEPAD', 'WORD', 'WORDPAD', 'PostScript', 'PDF', 'RTF', 'TeX', 'HTML'

If you have somehow managed to type a sequence into a word-processor (!) you should:

  • Save the sequence to a file as ASCII text (try selecting: File, SaveAs, Text)
  • Stop using word-processors to write sequences.
  • Investigate a sequence editor, such as mse
  • Investigate using simple text editors, such as pico, nedit or, at a pinch, wordpad

Now, repeat after me:
Microsoft WORD format is not a sequence format

EMBOSS programs will not read in anything which is held in Microsoft WORD files.

Sequence formats are ASCII TEXT.

They are the required arrangement of characters, symbols and keywords that specify what things such as the sequence, ID name, comments, etc. look like in the sequence entry and where in the entry the program should look to find them.

There are generally no hidden, unprintable 'control' characters in any sequence format (there are none in those that EMBOSS supports). All standard sequence formats can be printed out or viewed simply by displaying their file.

There are at least a couple of dozen sequence formats in existence at the moment. Some are much more common than others.

Formats were designed so as to be able to hold the sequence data and other information about the sequence.

Nearly every sequence analysis package written since programs were first used to read and write sequences has invented its own format. Except for EMBOSS.

Nearly every collection of sequences that dares call itself a database has stored its data in its own format.

A sequence does not require any sort of identification, but it certainly helps!

Most sequence formats include at least one form of ID name, usually placed somewhere at the top of the sequence format.

The simple format fasta has the ID name as the first word on its title line. For example the ID name 'xyz':


Custom annotation

Some users may want to add custom annotation beyond those mapped above. Currently there are two methods to do so however, the methods used for adding such annotation may change in the future, particularly if alignment Writer classes are introduced. In particular, do not rely on changing the global variables @WRITEORDER or %WRITEMAP as these may be made private at some point.

1) Use (and abuse) the 'custom' tag. The tagname for the object can differ from the tagname used to store the object in the AnnotationCollection.

2) Modify the global @WRITEORDER and %WRITEMAP.


Contents

The basic structure of a CRAM file is a series of containers, the first of which holds a compressed copy of the SAM header. Subsequent containers consist of a container Compression Header followed by a series of slices which in turn hold the alignment records themselves, formatted as a series of blocks.

Magic number Container
(SAM header)
Container
(Data)
. Container
(Data)
Container
(EOF)

Container
Header
Compression
Header
Slice . Slice

Slice
Header
Block Block . Block

CRAM constructs records from a set of data series, describing the components of an alignment. The container Compression Header specifies which data series is encoded in which block, what codec will be used, and any codec specific meta-data (for example a table of Huffman symbol code lengths). While data series can be mixed together within the same block, keeping them separate usually improves compression and provides the opportunity for efficient selective decoding where only some data types are required.

Selective access to a CRAM file is granted via the index (with file-name suffix ".crai"). On chromosome and position sorted data this indicates which region is covered by each slice. On unsorted data the index may be used to simply fetch the N th container. Selective decoding may also be achieved using the Compression Header to skip specified data series if partial records are required.

Year Version(s) Notes
2010-11 pre-CRAM Initial paper describing the reference based format. This did not use the name CRAM, but called it mzip. This software was implemented in Python as a prototype and demonstration of the basic concepts. [1]
2011-12 0.3 - 0.86 Vadim Zalunin of the European Bioinformatics Institute (EBI) produced the first implementation named CRAM as a package called CRAMtools, [8] written in the Java programming language.
2012 1.0 [9] Implemented in Java CRAMtools. [10]
2013 C implementation added to the Scramble [11] [5] tool, by James Bonfield of the Wellcome Sanger Institute.
2013 2.0 Changes included support for more than one reference per slice (useful with highly fragmented assemblies), better encoding of SAM auxiliary tags, splitting soft-clip and inserted bases into their own data-series, meta-data to track the number of records and bases per slice, and corrections to the BF (BAM flag) data-series.
2013 Added to htslib (0.2.0).
2014 2.1 [12] Added EOF blocks, to help identify truncated files.
2014 Added to htsjdk (1.127).
2014 3.0 [13] Inclusion of lzma and rANS codecs for block compression, along with multiple checksums for ensuring data integrity
2018 Javascript implementation as part of JBrowse [4] (1.15.0), by Rob Buels.

CRAM version 4.0 exists as a prototype in Scramble, [5] initially demonstrated in 2015, but has yet to be adopted as a standard.


The sequence alignment

Sequences are written one per line. The sequence name is written first, and after any number of whitespaces the sequence is written. Sequence names are typically in the form “name/start-end” or just “name”. Sequence letters may include any characters except whitespace. Gaps may be indicated by “.” or “-“. The “//” line indicates the end of the alignment.

Wrap-around alignments are allowed in principle, mainly for historical reasons, but are not used in e.g. Pfam. Wrapped alignments are discouraged since they are much harder to parse.


Discussion

The ViennaRNA Package has been a useful tool for the RNA bioinformatics community for almost two decades. Quite a few widely-used software tools and data analysis pipelines have been built upon this foundation, either incorporating calls to the interactive programs or directly interfacing to RNAlib . Numeric characteristics of secondary structures, such as Gibbs free energy ΔG, Minimum free energy (MFE), ensemble diversity or probabilities of MFE structures in the ensemble, have been widely used as features for machine learning classification, e.g. in microRNA precursor and target detection [91–94]. The non-coding RNA gene finder RNAz [95, 96], the snoRNA detector snoReport [97], and RNAstrand [98], a tool that predicts the reading direction of structured RNAs from a multiple sequence alignment, combine thermodynamic properties computed with RNAlib functions and a machine learning component. RNAsoup [99] takes advantage of the programs RNAfold , RNAalifold and some other tools provided by the ViennaRNA Package for a structural clustering of ncRNAs. The siRNA design program RNAxs [100] employs the site accessibility predictions offered by RNAplfold , as does IntaRNA [60], a program to predict RNA interaction sites. Several secondary structure prediction tools, such as CentroidFold [22], McCaskill-MEA [101], or RNAsalsa [102], use base pair probabilities predicted by RNAfold -p as input, while the LocARNA package [59] uses them for structural alignment. The motif-based comparison and alignment tool ExpaRNA [103] and the tree alignment program RNAforester [75] also rely on the algorithms provided by RNAlib . Since its initial publication [25], no comprehensive description [104] of the ViennaRNA Package has appeared. Release 2.0 now implements the latest energy model, provides many new and improved functionalities, and - as we hope - is even easier and more efficient to use due to a thread-safe architecture, an improved API, a more consistent set of options, and a much more detailed documentation. Care has been taken to ensure backward compatibility so that ViennaRNA Package 2.0 can be readily substituted for earlier versions.


NEW DEVELOPMENTS

The Rfam 10.0 “decimal” release

In order to keep Rfam as up-to-date as possible we aim to make regular releases of the database. These releases are snap-shots of the live, internal version of the database that are made publicly available via the websites and ftp. We have two types of release. A major release (indicated by an integer and a ‘.0’ in the version number e.g. ‘10.0’) usually involves updating the underlying sequence database, Rfamseq, to the latest version of EMBL and remapping all the seed sequences to the new databases. All the families are subsequently searched against the new database and, if necessary, re-thresholded. Minor releases are indicated by ‘.1’, ‘.2’, etc. in the version number e.g. ‘10.1’. These are usually made after adding many new families to the database built on the same underlying sequence database.

Rfam 10.0 was released in early 2010. This release included a major update to the underlying search algorithm, switching to a new version of Infernal, v1.0 ( 9 ). This required individually re-thresholding each Rfam family due to an important change in Infernal’s underlying scoring scheme from maximum likelihood alignment scores to summed scores over all possible alignments [i.e. switching from using the CYK algorithm to the Inside algorithm ( 11 )]. Additionally, the new version of Infernal reports estimates of the statistical significance of hits ( E -values) returned from database searches using Rfam 10.0 CM files. We also mapped all the families and searched a new version of Rfamseq based on EMBL 100 ( 10 ). The result of these and other internal improvements to our pipeline resulted in a 178% increase in the number of regions that Rfam covers, which contrasts with the rather modest increase in the size of Rfamseq by 40%. This has caused some of our alignments to become very large. For example, the tRNA full alignment now contains more than 1 million sequences. The amount of compute required for this release was roughly 5 CPU months to calibrate the models, 1 CPU year to run blast, 3 CPU years to run CM-searches (cmsearch) and 15 CPU days to produce CM-derived multiple sequence alignments (cmalign).

Evaluating the success of the Wikipedia community annotation model

One of the fundamental problems facing any biocuration effort is keeping the annotation of the entities stored in a database up to date with the current literature. Typically, the annotation of existing entries changes less quickly than new data are added, so entries become rapidly out-of-date.

In mid-2007, Rfam began experimenting with using Wikipedia as a means for storing and curating the textual annotation of RNA families. Three years on, the RNA family pages have received more than 9000 edits from more than 1000 unique users. Slightly over 1% of these edits have been recognized as possible vandalism ( Figure 1 ). The resulting marked-up annotation and curated references has dramatically improved the content of the Rfam database compared with the pre-2007 static text. The Wikipedia entries also help drive users to the Rfam website. Approximately 15% of all the web-traffic to http://rfam.sanger.ac.uk now comes via Wikipedia. As has been observed by others, a typical Google search for a biological term returns a Wikipedia entry among the top hits ( 12 , 13 ). From a curator’s viewpoint, Wikipedia is an excellent model to take advantage of as it includes a large community of contributors and comes with a number of user-friendly tools that help with basic editing, maintaining references and automated updates to pages with programs called bots. The large community also has other benefits, such as the well documented long-tail effect, where the majority of new content is added by a large number of editors, each of whom makes just a few edits ( 12 , 13 ). There are also dedicated editors who are obsessed with small but important details that an average curator may not have time to attend to, such as consistency of style, grammar and spelling. There are also editors who are dedicated to reverting obvious non-constructive edits, commonly referred to as `vandalism’, which are usually recognized and reverted within seconds. It is important to note that all edits are reviewed before appearing on the Rfam website, so the amount of overt vandalism reaching Rfam is 0. Given our positive experiences, we can highly recommend other curation efforts turning to Wikipedia for their annotation. However, it must be borne in mind that Wikipedia is built by consensus and to gain its benefits you will lose the tight control of the data allowed by in-house curation.

Edits for Wikipedia articles on RNA families. The cumulative number of edits since 1st January 2007 for the 733 Wikipedia articles that are associated with Rfam entries is shown in black. The total number of edits that were reverted or labeled as vandalism is shown in red. To mid-2010, there were just 106 of these. However, some reverted edits may have been well-intentioned but were deemed inappropriate for Wikipedia.

Edits for Wikipedia articles on RNA families. The cumulative number of edits since 1st January 2007 for the 733 Wikipedia articles that are associated with Rfam entries is shown in black. The total number of edits that were reverted or labeled as vandalism is shown in red. To mid-2010, there were just 106 of these. However, some reverted edits may have been well-intentioned but were deemed inappropriate for Wikipedia.

Rfam clans

One of the fundamental quality control steps that Rfam employs is that no two families can annotate the same nucleotide. This rule prevents us building two or more families for essentially the same entity. When building new Rfam families or extending an existing family, we sometimes find ourselves artificially increasing the threshold to avoid overlaps with another family or trimming the ends of families that have incorrect boundaries. We also find that a single alignment may not capture all the diversity of a group of homologous RNAs. To resolve some of these issues, we have borrowed the concept of a clan from the MEROPS and Pfam databases ( 14 , 15 ).

We have added 99 clans for the Rfam 10.0 release. These clans describe explicit relationships between families that either clearly share a common ancestor but are too divergent to be reasonably aligned or groups of families that could be aligned, but have clearly distinct functions and therefore should be kept as separate families. For example, the RNase P clan contains five homologous families RNase MRP, archeal RNase P, nuclear RNase P and the bacterial RNase P, types a and b. These RNAs are ribozymes involved in processing of pre-tRNA and pre-rRNA sequences. The RNase Ps are, however, notoriously difficult to align to each other. Furthermore, RNase P and RNase MRP are functionally distinct molecules ( 16 ). Another clan of interest is Glm this clan contains two homologous but functionally distinct bacterial small RNAs, GlmY and GlmZ, which act in a hierarchical fashion to regulate the translation of the glmS coding gene. GlmY activates expression of GlmZ which in turn de-sequesters the GlmS Shine-Dalgarno sequence via an anti-antisense interaction ( 17 ). The new clans mean that some of the internal quality control measures that Rfam uses can be relaxed for the clanned families. Primarily this means we can ignore our no-overlap rule, which has meant that in the past some of these families have had artificially high thresholds to avoid overlapping a related but distinct family.

In order to help assess the likelihood of a relationship between two or more families, we used a number of independent lines of evidence. These included sequence analysis based upon a SCOOP-like analysis for comparing overlapping hits from both profile hidden Markov model (HMM) and covariance model searches ( 18 ), the profile-profile comparison tool PRC ( 19 ) and literature searches for functional and evolutionary relationships. For the snoRNA and miRNA families, we were able to utilize some additional sources of information in order to establish homology. For the snoRNAs, we used some of the specialized snoRNA databases to confirm whether families targeted orthologous regions of rRNA, for many snoRNAs this helped to confirm a relationship between the families ( 20–23 ). For the miRNAs, we used the annotated seed region of the mature miRNA ( 24 ). If two or more miRNA families shared a significant amount of similarity in the seed region, and if they had further similarities identified by the sequence analysis tools, then these too were added to clans.

Species labels

The new set of seed and full alignments available via the website use descriptive species labels for sequence names rather than the more cryptic EMBL accessions and coordinates that were previously provided. The provenance of the sequence data is maintained by using ‘ #=GS ’ tags from Stockholm format ( 25 ) to provide a mapping back to EMBL accessions ( Figure 2 ). Stockholm is a versatile markup format for biological sequence alignments. It allows the markup of general file information, including references, comments and cross-links. It also allows the mark-up of regions of an alignment that cannot be aligned with tildes in the ‘ #=GC RF ’ lines.

An example Stockholm alignment for the UPSK pseudoknot from turnip yellow mosaic virus. The Stockholm alignment format is flexible enough to allow generic mark-up of file information with ‘ #=GF ' lines, sequence information with ‘ #=GS ' lines and column information with ‘ #=GC ' lines. Each is followed by at least a two-letter code giving an indication for what follows e.g. ‘ID' implies ‘identifier', ‘ AC ' implies ‘accession', ‘ AU ' implies ‘author', etc. All the commonly used tags are documented in the Wikipedia article for Stockholm alignment ( 25 ).

An example Stockholm alignment for the UPSK pseudoknot from turnip yellow mosaic virus. The Stockholm alignment format is flexible enough to allow generic mark-up of file information with ‘ #=GF ' lines, sequence information with ‘ #=GS ' lines and column information with ‘ #=GC ' lines. Each is followed by at least a two-letter code giving an indication for what follows e.g. ‘ID' implies ‘identifier', ‘ AC ' implies ‘accession', ‘ AU ' implies ‘author', etc. All the commonly used tags are documented in the Wikipedia article for Stockholm alignment ( 25 ).

Ontologies

An important feature for any biocuration effort is linking to related resources, for example, primary sequence resources databases, genomes and to specialized resources such as miRBase and the snoRNA databases. Recently, a number of groups have started developing controlled vocabularies for describing biological entities. Two efforts of particular relevance to Rfam are the sequence ontology (SO) and the gene ontology (GO) ( 26 , 27 ). For the majority of Rfam families, we have now added cross-links to both the SO and the GO. Many of these were provided by researchers at the functional RNA database ( 28 ). In the near future, we plan to introduce more ncRNA terms back into the ontologies. Until then the mapping will remain rather coarse-grained and closely related to the existing types Rfam uses as annotation ( 6 ). This mapping groups the RNAs into three main groups: ‘cis-reg’, ‘gene’ and ‘intron’ with subtypes such as ‘riboswitch’, ‘miRNA’ and ‘snoRNA’.

Future developments

New families in Rfam 10.1

For the forthcoming minor release of Rfam, we have added a number of new and notable families. Of particular note are the direct submissions of Stockholm formatted alignments and corresponding Wikipedia articles from the RNA community via the RNA families track at RNA Biology ( 8 ). This track has released much of the burden of building these new families from our curators, and the families produced have been built and annotated by experts and are therefore of high quality. Updated families from this route include RNase MRP, SRP, tmRNA and the U3 snoRNA ( 29–32 ). In addition, several families missing from past Rfam releases have been published, including the SmY RNA, the cyanobacterial RNA Yfr2, several Trypanosomatid snoRNAs, the self-splicing ribozyme GIR1, an influenza pseudoknot, the Staphylococcus small RNA RsaOG and a putative RNA antitoxin, ptaRNA1 ( 33–39 ). The ptaRNA1 article alerted us to the fact that Rfam contains none of the published and well-characterized RNA antitoxins such as sok and symE ( 40 ). These omissions will be remedied in Rfam 10.1. A growing class of cis -regulatory elements are the environmental sensors. These are generally structured 5′ UTR elements that change conformation in response to environmental changes such as temperature or pH this change subsequently influences the expression of the protein encoded in the host mRNA. We have added the first examples of a cold sensor and a pH sensor ( 41 , 42 ). Finally, we have received a dramatic number of submissions from a recent bioinformatic screen that was followed by a thorough analysis of the predictions largely based upon genomic context. This has resulted in more than 80 new additions to the database ( 43 ). Fortunately, the authors kindly provide both Stockholm formatted alignments and Wikipedia articles for these new families.

Covariance model pre-filters

A pressing issue for Rfam is the replacement of WU-BLAST as a pre-filter for searching the Rfamseq database. The legal rights to up-to-date versions of WU-BLAST were recently acquired by a commercial entity and the software can no longer be considered free in any meaningful sense. However, there have been several developments that should allow profile HMMs to be used as effective pre-filters for covariance model searches ( 44 ). Accelerated profile HMM searches are now available through the HMMER package ( 45–47 ). In the near future, Rfam will therefore be in a position to replace the current BLAST-based filters with accelerated profile HMMs.

Scale

Sequencing projects such as the Genome 10K ( 48 ) and other attempts to fill sequencing gaps in the tree of life ( 49 ) mean that most Rfam families will dramatically increase in depth in the near future. Large alignments already pose a considerable challenge when it comes to displaying or distributing the alignments themselves, or building and displaying related data such as species and phylogenetic trees. Novel techniques will need to be developed in order to deal with these and many other issues of scale. We look forward to working with the wider community to develop these new tools and techniques.


Bio.AlignIO package¶

Multiple sequence alignment input/output as alignment objects.

The Bio.AlignIO interface is deliberately very similar to Bio.SeqIO, and in fact the two are connected internally. Both modules use the same set of file format names (lower case strings). From the user’s perspective, you can read in a PHYLIP file containing one or more alignments using Bio.AlignIO, or you can read in the sequences within these alignmenta using Bio.SeqIO.

Bio.AlignIO is also documented at http://biopython.org/wiki/AlignIO and by a whole chapter in our tutorial:

Input¶

For the typical special case when your file or handle contains one and only one alignment, use the function Bio.AlignIO.read(). This takes an input file handle (or in recent versions of Biopython a filename as a string), format string and optional number of sequences per alignment. It will return a single MultipleSeqAlignment object (or raise an exception if there isn’t just one alignment):

For the general case, when the handle could contain any number of alignments, use the function Bio.AlignIO.parse(…) which takes the same arguments, but returns an iterator giving MultipleSeqAlignment objects (typically used in a for loop). If you want random access to the alignments by number, turn this into a list:

Most alignment file formats can be concatenated so as to hold as many different multiple sequence alignments as possible. One common example is the output of the tool seqboot in the PHLYIP suite. Sometimes there can be a file header and footer, as seen in the EMBOSS alignment output.

Output¶

Use the function Bio.AlignIO.write(…), which takes a complete set of Alignment objects (either as a list, or an iterator), an output file handle (or filename in recent versions of Biopython) and of course the file format:

If using a handle make sure to close it to flush the data to the disk:

In general, you are expected to call this function once (with all your alignments) and then close the file handle. However, for file formats like PHYLIP where multiple alignments are stored sequentially (with no file header and footer), then multiple calls to the write function should work as expected when using handles.

If you are using a filename, the repeated calls to the write functions will overwrite the existing file each time.

Conversion¶

The Bio.AlignIO.convert(…) function allows an easy interface for simple alignment file format conversions. Additionally, it may use file format specific optimisations so this should be the fastest way too.

In general however, you can combine the Bio.AlignIO.parse(…) function with the Bio.AlignIO.write(…) function for sequence file conversion. Using generator expressions provides a memory efficient way to perform filtering or other extra operations as part of the process.

File Formats¶

When specifying the file format, use lowercase strings. The same format names are also used in Bio.SeqIO and include the following:

  • clustal - Output from Clustal W or X, see also the module Bio.Clustalw which can be used to run the command line tool from Biopython.
  • emboss - EMBOSS tools’ “pairs” and “simple” alignment formats.
  • fasta - The generic sequence file format where each record starts with an identifer line starting with a “>” character, followed by lines of sequence.
  • fasta-m10 - For the pairswise alignments output by Bill Pearson’s FASTA tools when used with the -m 10 command line option for machine readable output.
  • ig - The IntelliGenetics file format, apparently the same as the MASE alignment format.
  • nexus - Output from NEXUS, see also the module Bio.Nexus which can also read any phylogenetic trees in these files.
  • phylip - Interlaced PHYLIP, as used by the PHLIP tools.
  • phylip-sequential - Sequential PHYLIP.
  • phylip-relaxed - PHYLIP like format allowing longer names.
  • stockholm - A richly annotated alignment file format used by PFAM.
  • mauve - Output from progressiveMauve/Mauve

Note that while Bio.AlignIO can read all the above file formats, it cannot write to all of them.

You can also use any file format supported by Bio.SeqIO, such as “fasta” or “ig” (which are listed above), PROVIDED the sequences in your file are all the same length.

Bio.AlignIO. convert ( in_file, in_format, out_file, out_format, alphabet=None ) ¶

Convert between two alignment files, returns number of alignments.

  • in_file - an input handle or filename
  • in_format - input file format, lower case string
  • output - an output handle or filename
  • out_file - output file format, lower case string
  • alphabet - optional alphabet to assume

NOTE - If you provide an output filename, it will be opened which will overwrite any existing file without warning. This may happen if even the conversion is aborted (e.g. an invalid out_format name is given).

Bio.AlignIO. parse ( handle, format, seq_count=None, alphabet=None ) ¶

Iterate over an alignment file as MultipleSeqAlignment objects.

  • handle - handle to the file, or the filename as a string (note older versions of Biopython only took a handle).
  • format - string describing the file format.
  • alphabet - optional Alphabet object, useful when the sequence type cannot be automatically inferred from the file itself (e.g. fasta, phylip, clustal)
  • seq_count - Optional integer, number of sequences expected in each alignment. Recommended for fasta format files.

If you have the file name in a string ‘filename’, use:

If you have a string ‘data’ containing the file contents, use:

Use the Bio.AlignIO.read() function when you expect a single record only.

Bio.AlignIO. read ( handle, format, seq_count=None, alphabet=None ) ¶

Turn an alignment file into a single MultipleSeqAlignment object.

  • handle - handle to the file, or the filename as a string (note older versions of Biopython only took a handle).
  • format - string describing the file format.
  • alphabet - optional Alphabet object, useful when the sequence type cannot be automatically inferred from the file itself (e.g. fasta, phylip, clustal)
  • seq_count - Optional integer, number of sequences expected in each alignment. Recommended for fasta format files.

If the handle contains no alignments, or more than one alignment, an exception is raised. For example, using a PFAM/Stockholm file containing one alignment:

If however you want the first alignment from a file containing multiple alignments this function would raise an exception.

You must use the Bio.AlignIO.parse() function if you want to read multiple records from the handle.

Bio.AlignIO. write ( alignments, handle, format ) ¶

Write complete set of alignments to a file.

  • alignments - A list (or iterator) of MultipleSeqAlignment objects, or a single alignment object.
  • handle - File handle object to write to, or filename as string (note older versions of Biopython only took a handle).
  • format - lower case string describing the file format to write.

You should close the handle after calling this function.

Returns the number of alignments written (as an integer).

© Copyright 1999-2017, The Biopython Contributors Revision 93a498d8 .


Example Run

In this example we first downloaded elephant sequences from Genbank ( approx 11MB ) into a file called elephant.fa.

Create a Database for RepeatModeler

RepeatModeler uses a NCBI BLASTDB or a ABBlast XDF database ( depending on the search engine used ) as input to the repeat modeling pipeline. A utility is provided to assist the user in creating a single database from several types of input structures.

Run "BuildDatabase" without any options in order to see the full documentation on this utility. There are several options which make it easier to import multiple sequence files into one database.

TIP: It is a good idea to place your datafiles and run this program suite from a local disk rather than over NFS. This will greatly improve runtime as the filesystem access is considerable

RepeatModeler runs several compute intensive programs on the input sequence. For best results run this on a single machine with a moderate amount of memory > 32GB and multiple processors.
Our setup is Xeon(R) CPU E5-2680 v4 @ 2.40GHz - 28 cores, 128GB RAM. To specify a run using 20 parallel jobs, and including the new LTR discovery pipeline:

The nohup is used on our machines when running long ( > 3-4 hour ) jobs. The log output is saved to a file and the process is backgrounded. For typical runtimes ( can be > 2 days with this configuration on a well assembled mammalian genome ) see the run statistics section of this file. It is important to save the log output for later usage.
It contains the random number generator seed so that the sampling process may be reproduced if necessary. In addition the log file contains details about the progress of the run for later assesment of peformance or debuging problems.

RepeatModeler produces a voluminous amount of temporary files stored in a directory created at runtime named like:

and remains after each run for debugging purposes or for the purpose of resuming runs if a failure occures. At the succesful completion of a run, two files are generated:

The seed alignment file is in a Dfam compatible Stockholm format and may be uploaded to the Dfam database by submiting the data to [email protected] In the near future we will provide a tool for uploading families directly to the database.

The fasta format is useful for running quick custom library searches using RepeatMasker. Ie.:

Other files produced in the working directory include:

If for some reason RepeatModeler fails, you may restart an analysis starting from the last round it was working on. The -recoverDir [ResultDir] option allows you to specify a diretory ( i.e RM_

. / ) where a previous run of RepeatModeler was working and it will automatically determine how to continue the analysis.


Custom annotation

Some users may want to add custom annotation beyond those mapped above. Currently there are two methods to do so however, the methods used for adding such annotation may change in the future, particularly if alignment Writer classes are introduced. In particular, do not rely on changing the global variables @WRITEORDER or %WRITEMAP as these may be made private at some point.

1) Use (and abuse) the 'custom' tag. The tagname for the object can differ from the tagname used to store the object in the AnnotationCollection.

2) Modify the global @WRITEORDER and %WRITEMAP.


Watch the video: Ενός λεπτού σιγή για τα θύματα της Στοκχόλμης (June 2022).


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    Your idea brilliantly

  6. Deasach

    I confirm. So happens. We can communicate on this theme. Here or in PM.



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