Package: LncFinder 1.1.6
LncFinder: LncRNA Identification and Analysis Using Heterologous Features
Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han S., et al. (2019) <doi:10.1093/bib/bby065>.
Authors:
LncFinder_1.1.6.tar.gz
LncFinder_1.1.6.zip(r-4.7)LncFinder_1.1.6.zip(r-4.6)LncFinder_1.1.6.zip(r-4.5)
LncFinder_1.1.6.tgz(r-4.6-any)LncFinder_1.1.6.tgz(r-4.5-any)
LncFinder_1.1.6.tar.gz(r-4.7-any)LncFinder_1.1.6.tar.gz(r-4.6-any)
LncFinder_1.1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
LncFinder/json (API)
| # Install 'LncFinder' in R: |
| install.packages('LncFinder', repos = c('https://han-siyu.r-universe.dev', 'https://cloud.r-project.org')) |
- demo_dataset - A demo of dataset
- demo_DNA.seq - A demo of DNA sequences
- demo_SS.seq - A demo of secondary structure sequences
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:fbef81fcc6. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 175 | ||
| source / vignettes | OK | 218 | ||
| linux-release-x86_64 | OK | 164 | ||
| macos-release-arm64 | OK | 127 | ||
| macos-oldrel-arm64 | OK | 128 | ||
| windows-devel | OK | 151 | ||
| windows-release | OK | 148 | ||
| windows-oldrel | OK | 145 | ||
| wasm-release | OK | 107 |
Exports:build_modelcompute_EIIPcompute_EucDistancecompute_FickettScorecompute_GCcompute_hexamerScorecompute_kmercompute_LogDistancecompute_pIextract_featuresfind_orfslnc_findermake_frequenciesmake_referFreqread_SSrun_RNAfoldsvm_cvsvm_tune
Dependencies:ade4caretclasscliclockcodetoolscpp11data.tablediagramdigestdplyre1071farverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsnlmennetnumDerivparallellypillarpixmappkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartS7scalessegmentedseqinrshapespsparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Build Users' Own Model | build_model |
| Extract the EIIP-derived features | compute_EIIP |
| Compute Euclidean Distance | compute_EucDistance |
| Compute Fickett TESTCODE Score | compute_FickettScore |
| Calculate GC content | compute_GC |
| Compute Hexamer Score | compute_hexamerScore |
| Compute _k_-mer Features | compute_kmer |
| Compute Logarithm Distance | compute_LogDistance |
| Compute Theoretical Isoelectric Point | compute_pI |
| A demo of dataset | demo_dataset |
| A demo of DNA sequences | demo_DNA.seq |
| A demo of secondary structure sequences | demo_SS.seq |
| Extract the Features | extract_features |
| Find ORFs | find_orfs |
| Long Non-coding RNA Identification | lnc_finder |
| Make the frequencies file for new classifier construction | make_frequencies |
| Make Frequencies File for Log.Dist, Euc.Dist, and hexamer score | make_referFreq |
| Read Secondary Structure Information | read_SS |
| Obtain the Secondary Structure Sequences Using RNAfold | run_RNAfold |
| _k_-fold Cross Validation for SVM | svm_cv |
| Parameter Tuning of SVM | svm_tune |
