發(fā)布時(shí)間：2018-04-21 01:31

  本文選題：河麂 + 鹿科��； 參考：《沈陽師范大學(xué)》2017年碩士論文

【摘要】：鹿類動(dòng)物在生物多樣性和生態(tài)系統(tǒng)中具有非常重要地位。由于生態(tài)環(huán)境的破壞、棲息地喪失和非法捕獵等因素的共同作用,其野生種群數(shù)量在急劇減少,有的甚至已經(jīng)滅絕,許多鹿類動(dòng)物已被列為瀕危物種。河麂(Hydropotes inermis)等鹿類動(dòng)物的保護(hù)還沒有引起足夠的重視。DNA條形碼序列能夠?qū)崿F(xiàn)從一個(gè)未知樣本到已知物種的鑒別,但這個(gè)目標(biāo)的實(shí)現(xiàn)需要可靠的數(shù)學(xué)運(yùn)算和分析方法。由于需要分類的生物類群不同或采用的DNA條形碼基因位點(diǎn)及序列長度的不同,在物種識(shí)別能力和效果上存在一定的差異。我們以河麂保護(hù)為目的,對(duì)鹿類動(dòng)物進(jìn)行分子識(shí)別分析方法比較研究,為河麂等鹿類動(dòng)物的皮毛、茸、肉等制品的鑒定提供基礎(chǔ)依據(jù)。本研究的樣本以糞便、毛發(fā)等無損傷取樣為主,提取基因組DNA。以線粒體細(xì)胞色素C氧化酶亞單元I基因(COI)片段作為DNA條形碼和細(xì)胞色素b基因標(biāo)記識(shí)別河麂等鹿類動(dòng)物,并進(jìn)行河麂在鹿類動(dòng)物的系統(tǒng)發(fā)育地位的分析。對(duì)鹿類動(dòng)物的8個(gè)物種30個(gè)樣本的COI基因和Cytb基因部分片段進(jìn)行了測(cè)序,得到700bp的COI序列和792bp長度的Cytb基因序列。從GenBank和BOLD數(shù)據(jù)庫下載了相關(guān)13個(gè)物種的43條COI序列,并以此作為DNA條形碼參考序列。依據(jù)相關(guān)理論和算法對(duì)測(cè)序獲得的COI序列樣本進(jìn)行分類指派分析,采用11種指派分析方法包括條形碼空隙探查法、鄰接樹法、條形碼邏輯公式算法、最近鄰算法、決策樹算法、規(guī)則RIPPER算法、隨機(jī)森林法、支持向量機(jī)算法、神經(jīng)網(wǎng)絡(luò)反向傳播算法、模糊數(shù)據(jù)算法、貝葉斯算法。結(jié)果顯示,這些方法在鹿類動(dòng)物分類中的解析能力和準(zhǔn)確性存在較大差異。隨機(jī)森林法和支持向量機(jī)法對(duì)鹿類動(dòng)物DNA條形碼分類最為可靠。綜合分析各種因素,作者認(rèn)為對(duì)各種方法表現(xiàn)性能影響最大的因素是分子數(shù)據(jù)多態(tài)性。通過增加基因位點(diǎn)、增加測(cè)序長度和樣本量可以改進(jìn)多數(shù)方法的預(yù)測(cè)性能,另外,選擇對(duì)分類樣本最適用的分析方法可以明顯提高分析質(zhì)量。為進(jìn)一步探討基因位點(diǎn)數(shù)和序列長度對(duì)識(shí)別效果的影響,以及為評(píng)估河麂的系統(tǒng)地位,我們將COI基因和Cytb基因合成1492bp長度的串聯(lián)數(shù)據(jù),采用最大似然法和貝葉斯法構(gòu)建系統(tǒng)發(fā)育樹。與單獨(dú)采用COI基因構(gòu)建的系統(tǒng)樹相比,序列樣本聚類關(guān)系基本一致,但置信值更高。河麂在鹿類動(dòng)物中屬于相對(duì)古老的物種,與狍的親緣關(guān)系最近。本次研究有目的地采集了河麂的2個(gè)地理亞種樣本,但物種識(shí)別分析和系統(tǒng)發(fā)育分析并沒有將二者明顯區(qū)分開,即未能將朝鮮河麂與華南河麂有效劃分,這可能反映了DNA條形碼分析在亞種級(jí)別的局限性,或這2個(gè)地理亞種分歧時(shí)間較晚。
[Abstract]:Deer play an important role in biodiversity and ecosystem. Due to the destruction of ecological environment, habitat loss and illegal hunting and other factors, its wild population is rapidly reduced, some have even been extinct, many deer have been listed as endangered species. The protection of deer, such as Hydropotes inermisis, has not attracted enough attention. DNA barcode sequences can identify species from an unknown sample to a known species, but the realization of this goal requires reliable mathematical operations and analysis methods. There are some differences in the ability and effect of species recognition because of the difference of the biological groups that need to be classified or the different DNA barcode gene loci and sequence length. We compared and studied the molecular recognition and analysis methods of deer in order to provide the basis for the identification of fur, antler, meat and other products of deer such as Heji. In this study, samples were collected mainly from feces and hair, and genomic DNAs were extracted. The mitochondrial cytochrome C oxidase subunit I gene was used as DNA bar code and cytochrome b gene marker to identify deer and other deer, and the phylogenetic status of Heji in deer was analyzed. The COI gene and Cytb gene partial fragment of 30 samples from 8 species of deer were sequenced. The COI sequence of 700bp and the Cytb gene sequence of 792bp length were obtained. 43 COI sequences of 13 species were downloaded from GenBank and BOLD databases and used as DNA barcode reference sequences. According to the relevant theories and algorithms, the COI sequence samples obtained by sequencing are classified and assigned. Eleven methods of assignment analysis are used, including barcode gap detection method, adjacency tree method, bar code logic formula algorithm, nearest neighbor algorithm, decision tree algorithm, etc. Rule RIPPER algorithm, Random Forest algorithm, support Vector Machine algorithm, Neural Network back Propagation algorithm, Fuzzy data algorithm, Bayesian algorithm. The results showed that there were significant differences in analytical ability and accuracy of these methods in deer classification. Random forest method and support vector machine method are the most reliable methods for DNA barcode classification of deer. Comprehensive analysis shows that molecular data polymorphism is the most important factor affecting the performance of various methods. The prediction performance of most methods can be improved by adding gene loci, sequencing length and sample size. In addition, selecting the most suitable analysis method for classified samples can obviously improve the analysis quality. In order to further study the effect of site number and sequence length on recognition, and to evaluate the systematic status of Heji, series data of the length of 1492bp synthesized by COI gene and Cytb gene were used. The phylogenetic tree was constructed by maximum likelihood method and Bayesian method. Compared with the system tree constructed by COI gene alone, the clustering relationship of sequence samples is basically consistent, but the confidence value is higher. River muntjac is a relatively old species in deer, and has the closest relationship with roe deer. In this study, two geographic subspecies samples of Heji were collected, but the species identification analysis and phylogenetic analysis did not distinguish them clearly, that is to say, they could not be effectively divided between Korean and South China river muntjac. This may reflect the limitation of DNA barcode analysis at subspecies level or the late divergence of these two geographic subspecies.
【學(xué)位授予單位】：沈陽師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：Q959.842

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