Information Management From the Interpretation of Al-Quran: Study on Tafsir Nur Al-Ihsan

The development of works in the field of Quranic interpretation has grown rapidly in this age. The work in this field has been written in various major languages of the world such as Arabic, English and Malay. The resulting works contain a lot of information quoted from the works written by previous authors. For example, the work of Tafsir Nur al-Ihsan written by Muhammad Said Omar contains a lot of information quoted from Tafsir al-Jalalyn, Tafsir al-Baydawi and Tafsir al-Jamal. Nevertheless, this study found that the information was widely quoted by the author until there was an inaccurate information being referred to by him. Therefore, a method or system should be practiced so that the information contained in the work of Tafsir Nur al-Ihsan can be managed well as well as avoid the mistakes of readers. One way to manage these resources is to identify the original work referred to by the author. The analysis of Tafsir Nur al-Ihsan used the genetic approach which was published by Goldman in studying the origin of the resources. This study referred the text from Tafsir Nur al-Ihsan and the texts of works that became author's references, which are Tafsir al-Jalalyn, Tafsir al-Jamal, Tafsir al-Baydawi, Tafsir al-Khazin, Tafsir al-Baghawi, Tafsir al-Tabari, Tafsir al-Qurtubi, Tafsir al-Razi, Tafsir al-Nasafi, Tafsir Ibn Kathir and Tafsir al-Tha’labi, and made a comparison of the texts to detect the similarities and differences. The result of the analysis proved that Syeikh Muhammad Sa’id implemented five methods; which are quoting the text in parallel, writing an abstract, removal of some of the words, adding explan-ation, and refuting part of the text. This study also revealed the purpose of Syeikh Muhammad Sa’id while creating his work is to strengthen the translations written. Thus, he successfully strengthens translation when the contents of the text are parallel to the reference. However, in certain places existed a text from the author's references contradicts with the content of Tafsir Nur al-Ihsan, which failed his attempt to strengthen the translation in certain part of the text. Key words: , , .

Statistical Tests for the Analysis of Human Genetic Linkage

There are many tests of inheritance based upon sibling information for diseases that have late onset. The N-test (Green et al. 1983) is one of these tests, which utilizes information from affected siblings. The N-test is the count in affected siblings of the most frequently occurring haplotype from the father plus the analogous count from the mother. When applied to haplotypes, the N-test excludes recombinant families from the analysis and assumes that parental genotypes are heterozygous. But in real world data sets, generally in Asia and particularly in Pakistan, the case of heterogeneity of parents genotyping were not exist and therefore recombinant families are found more frequently. In this study we modified the N-test to be based on alleles instead of haplotypes. This modified allele-based N-test can include all families (recombinant as well as nonrecombinant). We carried out a simulation study to compare the power of the allele-based N-test with the powers of the S a l l and S p a i r s nonparametric statistics as computed by MERLIN. The powers of the allelebased N-test, S a l l and S p a i r s statistics are identical to each other for iii affected sibships of size 2 and 3. For affected sibships of larger sizes, the powers of the S a l l and S p a i r s statistics are larger than the power of allelebased N-test. These simulation-based results are consistent with earlier results based on analytical computations. Gene-mapping studies regularly rely on examination for Mendelian transmission of marker alleles in a pedigree, as a way of screening for genotyping errors and mutations. For analysis of family data sets, it is usually necessary to resolve or remove the genotyping errors prior to analysis. At the Center of Inherited Disease Research (CIDR), to deal with their large-scale data flow, they formalized their data cleaning approach in a set of rules based on PedCheck output. We examine via carefully designed simulations that how well CIDR’s data cleaning rules work in practice. We found that genotype errors in siblings are detected more often than in parents for less polymorphic SNPs and vice versa for more polymorphic SNPs. Through computer simulation, we conclude that some of the CIDR’s rules work poorly in some situations and we suggest a set of modified data cleaning rules that may work better than CIDR’s rules.