public static void RNAMode(string measureType, string[] lines)
{
var uRNA = lines[1];
var wRNA = lines[2];
if (uRNA.Length % 3 != 0 || wRNA.Length % 3 != 0)
{
Console.WriteLine("Sekwencje RNA muszą byc podzielne przez 3.");
return;
}
var matrix = new double[21, 21];
for (var i = 3; i < lines.Length; i++)
{
var line = lines[i].Split(',');
for (var j = 0; j < 21; j++)
{
matrix[i - 3, j] = double.Parse(line[j]);
}
}
var u = RNAToAcidsMapper.GetAcids(uRNA);
var w = RNAToAcidsMapper.GetAcids(wRNA);
if (measureType == "rd")
{
var(measure, d) = MatrixBuilder.GlobalForDistanceRNA(u, w, matrix);
var optimalGlobalAlignment = GlobalAlignment.OptimalWithDistance(d, u, w);
var newRnaU = RNAToAcidsMapper.GetRNA(optimalGlobalAlignment.uResult, uRNA);
var newRnaW = RNAToAcidsMapper.GetRNA(optimalGlobalAlignment.wResult, wRNA);
PrintResult("Global", "Distance", uRNA, newRnaU, wRNA, newRnaW, measure);
PrintResult("Global", "Distance", u, optimalGlobalAlignment.uResult, w, optimalGlobalAlignment.wResult, measure);
var similarityMatrix = ConvertDistanceToSimilarity(matrix);
var(measureLocal, dLocal) = MatrixBuilder.LocalSimilarity(u, w, similarityMatrix);
var(uResult, wResult) = LocalAlignment.MaximalLocal(dLocal, u, w);
PrintResult("Local", "Distance", u, uResult, w, wResult, measureLocal);
}
else
{
var(measure, d) = MatrixBuilder.GlobalForSimilarityRNA(u, w, matrix);
var optimalGlobalAlignment = GlobalAlignment.OptimalWithSimilarity(d, u, w);
var newRnaU = RNAToAcidsMapper.GetRNA(optimalGlobalAlignment.uResult, uRNA);
var newRnaW = RNAToAcidsMapper.GetRNA(optimalGlobalAlignment.wResult, wRNA);
PrintResult("Global", "Similarity", uRNA, newRnaU, wRNA, newRnaW, measure);
PrintResult("Global", "Similarity", u, optimalGlobalAlignment.uResult, w, optimalGlobalAlignment.wResult, measure);
var(measureLocal, dLocal) = MatrixBuilder.LocalSimilarityRNA(u, w, matrix);
var(uResult, wResult) = LocalAlignment.MaximalLocal(dLocal, u, w);
var localRnaU = RNAToAcidsMapper.GetRNA(uResult, uRNA);
var localRnaW = RNAToAcidsMapper.GetRNA(wResult, wRNA);
PrintResult("Local", "Similarity", uRNA, localRnaU, wRNA, localRnaW, measureLocal);
}
}
public static void StandardMode(string measureType, string[] lines)
{
var u = lines[1];
var w = lines[2];
var matrix = new double[5, 5];
for (var i = 3; i < lines.Length; i++)
{
var line = lines[i].Split(',');
for (var j = 0; j < 5; j++)
{
matrix[i - 3, j] = double.Parse(line[j]);
}
}
if (measureType == "d")
{
var(measure, d) = MatrixBuilder.GlobalForDistance(u, w, matrix);
var optimalGlobalAlignment = GlobalAlignment.OptimalWithDistance(d, u, w);
PrintResult("Global", "Distance", u, optimalGlobalAlignment.uResult, w, optimalGlobalAlignment.wResult,
measure);
var similarityMatrix = ConvertDistanceToSimilarity(matrix);
//PrintMatrix(similarityMatrix);
var(measureLocal, dLocal) = MatrixBuilder.LocalSimilarity(u, w, similarityMatrix);
var(uResult, wResult) = LocalAlignment.MaximalLocal(dLocal, u, w);
PrintResult("Local", "Distance", u, uResult, w, wResult, measureLocal);
}
else
{
var(measure, d) = MatrixBuilder.GlobalForSimilarity(u, w, matrix);
var optimalGlobalAlignment = GlobalAlignment.OptimalWithSimilarity(d, u, w);
PrintResult("Global", "Similarity", u, optimalGlobalAlignment.uResult, w, optimalGlobalAlignment.wResult,
measure);
var(measureLocal, dLocal) = MatrixBuilder.LocalSimilarity(u, w, matrix);
var(uResult, wResult) = LocalAlignment.MaximalLocal(dLocal, u, w);
PrintResult("Local", "Similarity", u, uResult, w, wResult, measureLocal);
}
}
public static void WithPenalty(string measureType, string[] lines)
{
var u = lines[1];
var w = lines[2];
var matrix = new double[5, 5];
for (var i = 3; i < lines.Length; i++)
{
var line = lines[i].Split(',');
for (var j = 0; j < 5; j++)
{
matrix[i - 3, j] = double.Parse(line[j]);
}
}
var(measure, d) = MatrixBuilder.GlobalWithPenalty(u, w, matrix);
var optimalGlobalAlignment = GlobalAlignment.OptimalWithSimilarity(d, u, w);
PrintResult("Global", "With penalty", u, optimalGlobalAlignment.uResult, w, optimalGlobalAlignment.wResult,
measure);
}
