Plant_Annotation_TEs | Plant Genome Annotation - Transposable Elements | Data Labeling library
kandi X-RAY | Plant_Annotation_TEs Summary
kandi X-RAY | Plant_Annotation_TEs Summary
Transposable elements (TEs) are prominent features of a plant genome. During a plant genome annotation project, the TEs must be first identified before the structural gene annotation. After a proper TE annotation, all TE-derived regions in the genome must be masked (preferably soft-masked: repeats in lowercase rather than "N" or "X"). EDTA is one of the most powerful and complete TE annotation pipelines. However, EDTA has low power to annotate SINEs and LINEs. Using structural-based methods, the pipeline described here will create a non-redundant SINEs/LINEs library of your species' genome. These libraries can be further supplied to our modified version EDTA of the pipeline (--sine and --line flags). Moreover, it is known that the EDTA pipeline often generates many false positives TE predictions (mainly represented by TIRs elements).To avoid false positives, we developed an adaptation on the EDTA pipeline that tries to lower the false positive prediction of TIR elements. This adapted EDTA version also annotates non-autonomous LTR elements (e.g., LARD, TRIM, TR-GAG, and BARE-2). It comprehensively annotates SINE, LTR, and TIRs elements at their respective lineages. Furthermore, helitron elements are divided into autonomous (containing HEL domain) and non-autonomous (not containing HEL domain). The input is a fasta file containing the plant genome in chromosome-scale (preferably). The final result will be an augmented TE annotation (containing the outputs commonly generated by the EDTA pipeline), an adequately masked genome for structural gene annotation, and a complete TE report showing TE lineages abundances. Since this pipeline can retrieve complete CRM elements, it is possible to figure out the potential centromeric regions using other approaches with the trf tool. Important Notice 1: This pipeline was tested only on Ubuntu 20.20 and was made for Plant Genomes. Important Notice 2: This pipeline could be better and is under development for improvements.
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Trending Discussions on Data Labeling
QUESTION
I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.
What I've done so far is make this representation with the same enumerator class.
A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?
...ANSWER
Answered 2021-Dec-30 at 13:57Instead of using Enum
you can use a dict
mapping. You can avoid loops if you flatten your dataframe:
QUESTION
I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.
In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space
i have this:
...ANSWER
Answered 2020-Nov-16 at 07:24Try something like:
QUESTION
Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.
Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.
Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.
...ANSWER
Answered 2020-Oct-27 at 22:39Is the data behind virtual network by any chance?
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